essay on types of statistics

There are lies, damned lies, and statistics. —Mark Twain

What this handout is about

The purpose of this handout is to help you use statistics to make your argument as effectively as possible.

Introduction

Numbers are power. Apparently freed of all the squishiness and ambiguity of words, numbers and statistics are powerful pieces of evidence that can effectively strengthen any argument. But statistics are not a panacea. As simple and straightforward as these little numbers promise to be, statistics, if not used carefully, can create more problems than they solve.

Many writers lack a firm grasp of the statistics they are using. The average reader does not know how to properly evaluate and interpret the statistics he or she reads. The main reason behind the poor use of statistics is a lack of understanding about what statistics can and cannot do. Many people think that statistics can speak for themselves. But numbers are as ambiguous as words and need just as much explanation.

In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation (see our handout on how to quote .) But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.

But before we start writing statistics, let’s actually read a few.

Reading statistics

As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions. Below you will find a useful set of hard questions to ask of the numbers you find.

1. Does your evidence come from reliable sources?

This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources (for more information on finding reliable sources, please see our handout on evaluating print sources ). This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow. Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone.

2. What is the data’s background?

Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:

• Who asked the questions in the survey/poll?
• What, exactly, were the questions?
• Who interpreted the data?
• What issue prompted the survey/poll?
• What (policy/procedure) potentially hinges on the results of the poll?
• Who stands to gain from particular interpretations of the data?

All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. The goal of this exercise is not to find “pure, objective” data but to make any biases explicit, in order to more accurately interpret the evidence.

3. Are all data reported?

In most cases, the answer to this question is easy: no, they aren’t. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion. A reliable source should not exclude data that contradicts or weakens the information presented.

An example can be found on the evening news. If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day. Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? One possible explanation is that with fewer people on the road, even with the dangerous conditions, the number of accidents will be less than on an “average” day. The critical lesson here is that even when the general interpretation is “accurate,” the data may not actually be evidence for the particular interpretation. This means you have no way to verify if the interpretation is in fact correct.

There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question. You may have to look to another source or sources to find all the data you need.

4. Have the data been interpreted correctly?

If the author gives you her statistics, it is always wise to interpret them yourself. That is, while it is useful to read and understand the author’s interpretation, it is merely that—an interpretation. It is not the final word on the matter. Furthermore, sometimes authors (including you, so be careful) can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:

• Confusing correlation with causation. Just because two things vary together does not mean that one of them is causing the other. It could be nothing more than a coincidence, or both could be caused by a third factor. Such a relationship is called spurious.The classic example is a study that found that the more firefighters sent to put out a fire, the more damage the fire did. Yikes! I thought firefighters were supposed to make things better, not worse! But before we start shutting down fire stations, it might be useful to entertain alternative explanations. This seemingly contradictory finding can be easily explained by pointing to a third factor that causes both: the size of the fire. The lesson here? Correlation does not equal causation. So it is important not only to think about showing that two variables co-vary, but also about the causal mechanism.
• Ignoring the margin of error. When survey results are reported, they frequently include a margin of error. You might see this written as “a margin of error of plus or minus 5 percentage points.” What does this mean? The simple story is that surveys are normally generated from samples of a larger population, and thus they are never exact. There is always a confidence interval within which the general population is expected to fall. Thus, if I say that the number of UNC students who find it difficult to use statistics in their writing is 60%, plus or minus 4%, that means, assuming the normal confidence interval of 95%, that with 95% certainty we can say that the actual number is between 56% and 64%.

Why does this matter? Because if after introducing this handout to the students of UNC, a new poll finds that only 56%, plus or minus 3%, are having difficulty with statistics, I could go to the Writing Center director and ask for a raise, since I have made a significant contribution to the writing skills of the students on campus. However, she would no doubt point out that a) this may be a spurious relationship (see above) and b) the actual change is not significant because it falls within the margin of error for the original results. The lesson here? Margins of error matter, so you cannot just compare simple percentages.

Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else’s data. Thus, you need to consider not only your source, but the author’s sources as well.

Writing statistics

As you write with statistics, remember your own experience as a reader of statistics. Don’t forget how frustrated you were when you came across unclear statistics and how thankful you were to read well-presented ones. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers. Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation.

As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them.

1. The mistake of the “average” writer

Nobody wants to be average. Moreover, nobody wants to just see the word “average” in a piece of writing. Why? Because nobody knows exactly what it means. There are not one, not two, but three different definitions of “average” in statistics, and when you use the word, your reader has only a 33.3% chance of guessing correctly which one you mean.

For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, 38

• Mean (arithmetic mean) This may be the most average definition of average (whatever that means). This is the weighted average—a total of all numbers included divided by the quantity of numbers represented. Thus the mean of the above set of numbers is 5+5+5+8+12+14+21+33+38, all divided by 9, which equals 15.644444444444 (Wow! That is a lot of numbers after the decimal—what do we do about that? Precision is a good thing, but too much of it is over the top; it does not necessarily make your argument any stronger. Consider the reasonable amount of precision based on your input and round accordingly. In this case, 15.6 should do the trick.)
• Median Depending on whether you have an odd or even set of numbers, the median is either a) the number midway through an odd set of numbers or b) a value halfway between the two middle numbers in an even set. For the above set (an odd set of 9 numbers), the median is 12. (5, 5, 5, 8 < 12 < 14, 21, 33, 38)
• Mode The mode is the number or value that occurs most frequently in a series. If, by some cruel twist of fate, two or more values occur with the same frequency, then you take the mean of the values. For our set, the mode would be 5, since it occurs 3 times, whereas all other numbers occur only once.

As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average he or she is using. Otherwise, confusion will inevitably ensue.

2. Match your facts with your questions

Be sure that your statistics actually apply to the point/argument you are making. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics.

Perhaps an example would help illustrate this point. Your professor hands back the midterm. The grades are distributed as follows:

The professor felt that the test must have been too easy, because the average (median) grade was a 95.

When a colleague asked her about how the midterm grades came out, she answered, knowing that her classes were gaining a reputation for being “too easy,” that the average (mean) grade was an 80.

When your parents ask you how you can justify doing so poorly on the midterm, you answer, “Don’t worry about my 63. It is not as bad as it sounds. The average (mode) grade was a 58.”

I will leave it up to you to decide whether these choices are appropriate. Selecting the appropriate facts or statistics will help your argument immensely. Not only will they actually support your point, but they will not undermine the legitimacy of your position. Think about how your parents will react when they learn from the professor that the average (median) grade was 95! The best way to maintain precision is to specify which of the three forms of “average” you are using.

3. Show the entire picture

Sometimes, you may misrepresent your evidence by accident and misunderstanding. Other times, however, misrepresentation may be slightly less innocent. This can be seen most readily in visual aids. Do not shape and “massage” the representation so that it “best supports” your argument. This can be achieved by presenting charts/graphs in numerous different ways. Either the range can be shortened (to cut out data points which do not fit, e.g., starting a time series too late or ending it too soon), or the scale can be manipulated so that small changes look big and vice versa. Furthermore, do not fiddle with the proportions, either vertically or horizontally. The fact that USA Today seems to get away with these techniques does not make them OK for an academic argument.

Charts A, B, and C all use the same data points, but the stories they seem to be telling are quite different. Chart A shows a mild increase, followed by a slow decline. Chart B, on the other hand, reveals a steep jump, with a sharp drop-off immediately following. Conversely, Chart C seems to demonstrate that there was virtually no change over time. These variations are a product of changing the scale of the chart. One way to alleviate this problem is to supplement the chart by using the actual numbers in your text, in the spirit of full disclosure.

Another point of concern can be seen in Charts D and E. Both use the same data as charts A, B, and C for the years 1985-2000, but additional time points, using two hypothetical sets of data, have been added back to 1965. Given the different trends leading up to 1985, consider how the significance of recent events can change. In Chart D, the downward trend from 1990 to 2000 is going against a long-term upward trend, whereas in Chart E, it is merely the continuation of a larger downward trend after a brief upward turn.

One of the difficulties with visual aids is that there is no hard and fast rule about how much to include and what to exclude. Judgment is always involved. In general, be sure to present your visual aids so that your readers can draw their own conclusions from the facts and verify your assertions. If what you have cut out could affect the reader’s interpretation of your data, then you might consider keeping it.

4. Give bases of all percentages

Because percentages are always derived from a specific base, they are meaningless until associated with a base. So even if I tell you that after this reading this handout, you will be 23% more persuasive as a writer, that is not a very meaningful assertion because you have no idea what it is based on—23% more persuasive than what?

Let’s look at crime rates to see how this works. Suppose we have two cities, Springfield and Shelbyville. In Springfield, the murder rate has gone up 75%, while in Shelbyville, the rate has only increased by 10%. Which city is having a bigger murder problem? Well, that’s obvious, right? It has to be Springfield. After all, 75% is bigger than 10%.

Hold on a second, because this is actually much less clear than it looks. In order to really know which city has a worse problem, we have to look at the actual numbers. If I told you that Springfield had 4 murders last year and 7 this year, and Shelbyville had 30 murders last year and 33 murders this year, would you change your answer? Maybe, since 33 murders are significantly more than 7. One would certainly feel safer in Springfield, right?

Not so fast, because we still do not have all the facts. We have to make the comparison between the two based on equivalent standards. To do that, we have to look at the per capita rate (often given in rates per 100,000 people per year). If Springfield has 700 residents while Shelbyville has 3.3 million, then Springfield has a murder rate of 1,000 per 100,000 people, and Shelbyville’s rate is merely 1 per 100,000. Gadzooks! The residents of Springfield are dropping like flies. I think I’ll stick with nice, safe Shelbyville, thank you very much.

Percentages are really no different from any other form of statistics: they gain their meaning only through their context. Consequently, percentages should be presented in context so that readers can draw their own conclusions as you emphasize facts important to your argument. Remember, if your statistics really do support your point, then you should have no fear of revealing the larger context that frames them.

Important questions to ask (and answer) about statistics

• Is the question being asked relevant?
• Do the data come from reliable sources?
• Margin of error/confidence interval—when is a change really a change?
• Are all data reported, or just the best/worst?
• Are the data presented in context?
• Have the data been interpreted correctly?
• Does the author confuse correlation with causation?

Now that you have learned the lessons of statistics, you have two options. Use this knowledge to manipulate your numbers to your advantage, or use this knowledge to better understand and use statistics to make accurate and fair arguments. The choice is yours. Nine out of ten writers, however, prefer the latter, and the other one later regrets his or her decision.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Types of Statistics

Definition of Statistics

Any raw Data, when collected and organized in the form of numerical or tables, is known as Statistics. Statistics is also the mathematical study of the probability of events occurring based on known quantitative Data or a Collection of Data.

Statistics attempts to infer the properties of a large Collection of Data from inspection of a sample of the Collection thereby allowing educated guesses to be made with a minimum of expense. There are generally 3 kinds of averages commonly used in Statistics. They are: (i) Mean, (ii) Median, and (iii) Mode. 

Statistics is the study of Data Collection, Analysis, Interpretation, Presentation, and organizing in a specific way. Mathematical methods used for different analytics include mathematical Analysis, linear algebra, stochastic Analysis, the theory of measure-theoretical probability, and differential equations. Collecting, classifying, organizing, and displaying numerical Data is associated with Statistics. This helps one to grasp different outcomes from it and foresee several possibilities of various events. Statistics discuss information, observations, and Data in the form of numerical Data. We are able to find different indicators of central tendencies and the divergence of various values from the center with the help of Statistics. 

The ability to analyze and interpret statistical Data is a vital skill for researchers and professionals from a wide variety of disciplines. You may need to make decisions on the basis of statistical Data, interpret statistical Data in research papers, do your own research, and interpret the Data.

There are two kinds of Statistics, which are descriptive Statistics and inferential Statistics. In descriptive Statistics, the Data or Collection Data are described in a summarized way, whereas in inferential Statistics, we make use of it in order to explain the descriptive kind. Both of them are used on a large scale. Also, there is another kind of Statistics where descriptive transitions into inferential Statistics.

Statistics is mainly divided into the following two categories. 

Descriptive Statistics

Inferential statistics.

In the descriptive Statistics, the Data is described in a summarized way. The summarization is done from the sample of the population using different parameters like Mean or standard deviation. Descriptive Statistics are a way of using charts, graphs, and summary measures to organize, represent, and explain a set of Data. 

Data is typically arranged and displayed in tables or graphs summarizing details such as histograms, pie charts, bars or scatter plots.

Descriptive Statistics are just descriptive and thus do not require normalization beyond the Data collected.

In the Inferential Statistics, we try to interpret the Meaning of descriptive Statistics. After the Data has been collected, analyzed, and summarised we use Inferential Statistics to describe the Meaning of the collected Data. 

Inferential Statistics use the probability principle to assess whether trends contained in the research sample can be generalized to the larger population from which the sample originally comes.

Inferential Statistics are intended to test hypotheses and investigate relationships between variables and can be used to make population predictions.

Inferential Statistics are used to draw conclusions and inferences, i.e., to make valid generalizations from samples.

In a class, the Data is the set of marks obtained by 50 students. Now when we take out the Data average, the result is the average of 50 students’ marks. If the average marks obtained by 50 students are 88 out of 100, on the basis of the outcome, we will draw a conclusion. 

Mean, Median and Mode in Statistics

Mean: Mean is considered the arithmetic average of a Data set that is found by adding the numbers in a set and dividing by the number of observations in the Data set. 

Median: The middle number in the Data set while listed in either ascending or descending order is the Median. 

Mode: The number that occurs the most in a Data set and ranges between the highest and lowest value is the Mode. 

For n number of observations, we have

Mean = \[\overline {x} = \frac{\sum x}{n} = \sum {x}{n} \]

Median = \[ \frac{\left[ \frac {n}{2} + 1 \right ]^{th} term}{2}\] if n is odd.

Median = \[\frac {\left[ \frac {n}{2} \right ]^{th} term +  \left[ \frac {n}{2} + 1 \right ]^{th} term }{2}\] if n is even.

Mode = The value which occurs most frequently

Measures of Dispersion in Statistics

The measures of central tendency do not suffice to describe the complete information about a given Data. Therefore, the variability is described by a value called the measure of dispersion. 

The different measures of dispersion include:

The range in Statistics is calculated as the difference between the maximum value and the minimum value of the Data points.

The quartile deviation that measures the absolute measure of dispersion. The Data points are divided into 3 quarters. Find the Median of the Data points. The Median of the Data points to the left of this Median is said to be the upper quartile and the Median of the Data points to the right of this Median is said to be the lower quartile. Upper quartile - lower quartile is the interquartile range. Half of this is the quartile deviation.

The Mean deviation is the statistical measure to determine the average of the absolute difference between the items in a distribution and the Mean or Median of that series.

The standard deviation is the measure of the amount of variation of a set of values.

Solved Example

1. What is the probability of getting two tails and one head, when 3 coins are tossed at a time?

Step 1: Number of possible outcomes when one coin is tossed = 2. Outcomes are HHH and TTT. 

Step 2: The possible outcomes, when 3 coins are tossed are {TTT, THT, TTH, THH, HHT, HTH, HTT, HHH}. 

Step 3: Number of favorable outcomes = 3. Favorable outcomes are THT, TTH, HTT. 

Step 4: Substitute.

Step 5: So, the probability of getting two tails and one head is \[\frac{3}{8}\].

Correct answer: (B) \[\frac{3}{8}\].

Stages of Statistics

Collection of Data:

This is the first step of statistical Analysis where we collect the Data using different methods depending upon the case.

Organizing the Collected Data: 

In the next step, we organize the collected Data in a Meaningful manner. All the Data is made easier to understand.  

Presentation of Data: 

In the third step we simplify the Data. These Data are presented in the form of tables, graphs, and diagrams.

Analysis of the Data: 

Analysis is required to get the right results. It is often carried out using measures of central tendencies, measures of dispersion, correlation, regression, and interpolation.

Interpretation of Data: 

In this last stage, conclusions are enacted. Use of comparisons is made. On this basis, forecasting is made.

Uses of Statistics

Statistics helps to obtain appropriate quantitative Data. 

Statistics helps to present complex Data for the simple and consistent Interpretation of the Data in a suitable tabular, diagrammatic, and graphic form.

Statistics help to explain the nature and pattern of variability through quantitative observations of a phenomenon.

Statistics help to depict the Data in tabular form, or in a graphical form in order to understand it properly. 

Applications of Statistics

Statistics is used in Machine Learning and Data Mining.

Statistics is used in Mathematics.

Statistics is used in Economics. 

FAQs on Types of Statistics

1. What is Statistics?

Statistics is basically the Collection of Data and study of analyzing, interpreting, and organizing the collected Data in a Meaningful manner. Statistics is used in a number of fields such as psychology, business, social sciences, humanities, government, and manufacturing. Using a sample procedure or some other process, statistical Data is obtained. Statistics makes a set of Data more easily understandable. It is a branch of mathematics that analyses Data and then uses it to solve different types of problems related to the Data.

2. How Do We Apply Statistics in Math?

Statistics is a part of Applied Mathematics that makes use of probability theory to simplify the sample collected Data. Data refers to a Collection of facts, like numbers, words, measurements, observations, and many more. Statistical Analysis involves the process of collecting and analyzing Data and then summarizing the Data in a mathematical form. Statistics is a part of Applied Mathematics that uses the principle of probability to simplify the sample Data we collect. It helps to characterize the probability that the Data generalizations are valid. We refer to this as statistical inference. 

3. What are the methods of Statistics?

The methods of Statistics comprises many steps where we collect, summarize, analyze, and interpret variable numerical Data. Some of the methods are:

Data Collection

Data summarization

Statistical Analysis

There are two types of Data: (i) qualitative Data & (ii) quantitative Data. Qualitative Data refers to descriptive Data. On the other hand, quantitative Data refers to numerical information. There are two kinds of quantitative Data which are discrete Data and continuous Data. Discrete Data refers to one where there is a specific fixed value which we can count whereas continuous Data is where the Data isn’t fixed and comprises a range of Data which we can measure.

4. What are the real-life examples of Statistics?

The following are the real-life examples of Statistics:

Suppose you need to find the Mean of the marks obtained by each student in the class whose strength is 50. The average value here is the Statistics of the marks obtained.

If you want to find how many members are employed in a city which is populated with 25 lakh people, a survey for 1000 people will be taken as a sample. Based on that, the Data is created, which is the statistic. 

5. Where can I get a detailed explanation of Statistics in maths?

Students can get a detailed explanation of Statistics in maths on Vedantu. Vedantu has provided detailed explanation of Statistics from maths including definition of Statistics, types of Statistics, descriptive Statistics, Inferential Statistics, Stages of Statistics, Uses of Statistics, Applications of Statistics, methods of Statistics, real-life examples of Statistics and solved examples. Students can browse various topics of mathematics on Vedantu and find detailed explanations for each topic for better understanding of mathematical concepts and accelerate their exam preparation for maths.

Understanding and Using Statistical Methods

Statistics is a set of tools used to organize and analyze data. Data must either be numeric in origin or transformed by researchers into numbers. For instance, statistics could be used to analyze percentage scores English students receive on a grammar test: the percentage scores ranging from 0 to 100 are already in numeric form. Statistics could also be used to analyze grades on an essay by assigning numeric values to the letter grades, e.g., A=4, B=3, C=2, D=1, and F=0.

Employing statistics serves two purposes, (1) description and (2) prediction. Statistics are used to describe the characteristics of groups. These characteristics are referred to as variables . Data is gathered and recorded for each variable. Descriptive statistics can then be used to reveal the distribution of the data in each variable.

Statistics is also frequently used for purposes of prediction. Prediction is based on the concept of generalizability : if enough data is compiled about a particular context (e.g., students studying writing in a specific set of classrooms), the patterns revealed through analysis of the data collected about that context can be generalized (or predicted to occur in) similar contexts. The prediction of what will happen in a similar context is probabilistic . That is, the researcher is not certain that the same things will happen in other contexts; instead, the researcher can only reasonably expect that the same things will happen.

Prediction is a method employed by individuals throughout daily life. For instance, if writing students begin class every day for the first half of the semester with a five-minute freewriting exercise, then they will likely come to class the first day of the second half of the semester prepared to again freewrite for the first five minutes of class. The students will have made a prediction about the class content based on their previous experiences in the class: Because they began all previous class sessions with freewriting, it would be probable that their next class session will begin the same way. Statistics is used to perform the same function; the difference is that precise probabilities are determined in terms of the percentage chance that an outcome will occur, complete with a range of error. Prediction is a primary goal of inferential statistics.

Revealing Patterns Using Descriptive Statistics

Descriptive statistics, not surprisingly, "describe" data that have been collected. Commonly used descriptive statistics include frequency counts, ranges (high and low scores or values), means, modes, median scores, and standard deviations. Two concepts are essential to understanding descriptive statistics: variables and distributions .

Statistics are used to explore numerical data (Levin, 1991). Numerical data are observations which are recorded in the form of numbers (Runyon, 1976). Numbers are variable in nature, which means that quantities vary according to certain factors. For examples, when analyzing the grades on student essays, scores will vary for reasons such as the writing ability of the student, the students' knowledge of the subject, and so on. In statistics, these reasons are called variables. Variables are divided into three basic categories:

Nominal Variables

Nominal variables classify data into categories. This process involves labeling categories and then counting frequencies of occurrence (Runyon, 1991). A researcher might wish to compare essay grades between male and female students. Tabulations would be compiled using the categories "male" and "female." Sex would be a nominal variable. Note that the categories themselves are not quantified. Maleness or femaleness are not numerical in nature, rather the frequencies of each category results in data that is quantified -- 11 males and 9 females.

Ordinal Variables

Ordinal variables order (or rank) data in terms of degree. Ordinal variables do not establish the numeric difference between data points. They indicate only that one data point is ranked higher or lower than another (Runyon, 1991). For instance, a researcher might want to analyze the letter grades given on student essays. An A would be ranked higher than a B, and a B higher than a C. However, the difference between these data points, the precise distance between an A and a B, is not defined. Letter grades are an example of an ordinal variable.

Interval Variables

Interval variables score data. Thus the order of data is known as well as the precise numeric distance between data points (Runyon, 1991). A researcher might analyze the actual percentage scores of the essays, assuming that percentage scores are given by the instructor. A score of 98 (A) ranks higher than a score of 87 (B), which ranks higher than a score of 72 (C). Not only is the order of these three data points known, but so is the exact distance between them -- 11 percentage points between the first two, 15 percentage points between the second two and 26 percentage points between the first and last data points.

Distributions

A distribution is a graphic representation of data. The line formed by connecting data points is called a frequency distribution. This line may take many shapes. The single most important shape is that of the bell-shaped curve, which characterizes the distribution as "normal." A perfectly normal distribution is only a theoretical ideal. This ideal, however, is an essential ingredient in statistical decision-making (Levin, 1991). A perfectly normal distribution is a mathematical construct which carries with it certain mathematical properties helpful in describing the attributes of the distribution. Although frequency distribution based on actual data points seldom, if ever, completely matches a perfectly normal distribution, a frequency distribution often can approach such a normal curve.

The closer a frequency distribution resembles a normal curve, the more probable that the distribution maintains those same mathematical properties as the normal curve. This is an important factor in describing the characteristics of a frequency distribution. As a frequency distribution approaches a normal curve, generalizations about the data set from which the distribution was derived can be made with greater certainty. And it is this notion of generalizability upon which statistics is founded. It is important to remember that not all frequency distributions approach a normal curve. Some are skewed. When a frequency distribution is skewed, the characteristics inherent to a normal curve no longer apply.

Making Predictions Using Inferential Statistics

Inferential statistics are used to draw conclusions and make predictions based on the descriptions of data. In this section, we explore inferential statistics by using an extended example of experimental studies. Key concepts used in our discussion are probability, populations, and sampling.

Experiments

A typical experimental study involves collecting data on the behaviors, attitudes, or actions of two or more groups and attempting to answer a research question (often called a hypothesis). Based on the analysis of the data, a researcher might then attempt to develop a causal model that can be generalized to populations.

A question that might be addressed through experimental research might be "Does grammar-based writing instruction produce better writers than process-based writing instruction?" Because it would be impossible and impractical to observe, interview, survey, etc. all first-year writing students and instructors in classes using one or the other of these instructional approaches, a researcher would study a sample – or a subset – of a population. Sampling – or the creation of this subset of a population – is used by many researchers who desire to make sense of some phenomenon.

To analyze differences in the ability of student writers who are taught in each type of classroom, the researcher would compare the writing performance of the two groups of students.

Dependent Variables

In an experimental study, a variable whose score depends on (or is determined or caused by) another variable is called a dependent variable. For instance, an experiment might explore the extent to which the writing quality of final drafts of student papers is affected by the kind of instruction they received. In this case, the dependent variable would be writing quality of final drafts.

Independent Variables

In an experimental study, a variable that determines (or causes) the score of a dependent variable is called an independent variable. For instance, an experiment might explore the extent to which the writing quality of final drafts of student papers is affected by the kind of instruction they received. In this case, the independent variable would be the kind of instruction students received.

Probability

Beginning researchers most often use the word probability to express a subjective judgment about the likelihood, or degree of certainty, that a particular event will occur. People say such things as: "It will probably rain tomorrow." "It is unlikely that we will win the ball game." It is possible to assign a number to the event being predicted, a number between 0 and 1, which represents degree of confidence that the event will occur. For example, a student might say that the likelihood an instructor will give an exam next week is about 90 percent, or .9. Where 100 percent, or 1.00, represents certainty, .9 would mean the student is almost certain the instructor will give an exam. If the student assigned the number .6, the likelihood of an exam would be just slightly greater than the likelihood of no exam. A rating of 0 would indicate complete certainty that no exam would be given(Shoeninger, 1971).

The probability of a particular outcome or set of outcomes is called a p-value . In our discussion, a p-value will be symbolized by a p followed by parentheses enclosing a symbol of the outcome or set of outcomes. For example, p(X) should be read, "the probability of a given X score" (Shoeninger). Thus p(exam) should be read, "the probability an instructor will give an exam next week."

A population is a group which is studied. In educational research, the population is usually a group of people. Researchers seldom are able to study every member of a population. Usually, they instead study a representative sample – or subset – of a population. Researchers then generalize their findings about the sample to the population as a whole.

Sampling is performed so that a population under study can be reduced to a manageable size. This can be accomplished via random sampling, discussed below, or via matching.

Random sampling is a procedure used by researchers in which all samples of a particular size have an equal chance to be chosen for an observation, experiment, etc (Runyon and Haber, 1976). There is no predetermination as to which members are chosen for the sample. This type of sampling is done in order to minimize scientific biases and offers the greatest likelihood that a sample will indeed be representative of the larger population. The aim here is to make the sample as representative of the population as possible. Note that the closer a sample distribution approximates the population distribution, the more generalizable the results of the sample study are to the population. Notions of probability apply here. Random sampling provides the greatest probability that the distribution of scores in a sample will closely approximate the distribution of scores in the overall population.

Matching is a method used by researchers to gain accurate and precise results of a study so that they may be applicable to a larger population. After a population has been examined and a sample has been chosen, a researcher must then consider variables, or extrinsic factors, that might affect the study. Matching methods apply when researchers are aware of extrinsic variables before conducting a study. Two methods used to match groups are:

Precision Matching

In precision matching , there is an experimental group that is matched with a control group. Both groups, in essence, have the same characteristics. Thus, the proposed causal relationship/model being examined allows for the probabilistic assumption that the result is generalizable.

Frequency Distribution

Frequency distribution is more manageable and efficient than precision matching. Instead of one-to-one matching that must be administered in precision matching, frequency distribution allows the comparison of an experimental and control group through relevant variables. If three Communications majors and four English majors are chosen for the control group, then an equal proportion of three Communications major and four English majors should be allotted to the experiment group. Of course, beyond their majors, the characteristics of the matched sets of participants may in fact be vastly different.

Although, in theory, matching tends to produce valid conclusions, a rather obvious difficulty arises in finding subjects which are compatible. Researchers may even believe that experimental and control groups are identical when, in fact, a number of variables have been overlooked. For these reasons, researchers tend to reject matching methods in favor of random sampling.

Statistics can be used to analyze individual variables, relationships among variables, and differences between groups. In this section, we explore a range of statistical methods for conducting these analyses.

Statistics can be used to analyze individual variables, relationships among variables, and differences between groups.

Analyzing Individual Variables

The statistical procedures used to analyze a single variable describing a group (such as a population or representative sample) involve measures of central tendency and measures of variation . To explore these measures, a researcher first needs to consider the distribution , or range of values of a particular variable in a population or sample. Normal distribution occurs if the distribution of a population is completely normal. When graphed, this type of distribution will look like a bell curve; it is symmetrical and most of the scores cluster toward the middle. Skewed Distribution simply means the distribution of a population is not normal. The scores might cluster toward the right or the left side of the curve, for instance. Or there might be two or more clusters of scores, so that the distribution looks like a series of hills.

Once frequency distributions have been determined, researchers can calculate measures of central tendency and measures of variation. Measures of central tendency indicate averages of the distribution, and measures of variation indicate the spread, or range, of the distribution (Hinkle, Wiersma and Jurs 1988).

Measures of Central Tendency

Central tendency is measured in three ways: mean , median and mode . The mean is simply the average score of a distribution. The median is the center, or middle score within a distribution. The mode is the most frequent score within a distribution. In a normal distribution, the mean, median and mode are identical.

Measures of Variation

Measures of variation determine the range of the distribution, relative to the measures of central tendency. Where the measures of central tendency are specific data points, measures of variation are lengths between various points within the distribution. Variation is measured in terms of range, mean deviation, variance, and standard deviation (Hinkle, Wiersma and Jurs 1988).

The range is the distance between the lowest data point and the highest data point. Deviation scores are the distances between each data point and the mean.

Mean deviation is the average of the absolute values of the deviation scores; that is, mean deviation is the average distance between the mean and the data points. Closely related to the measure of mean deviation is the measure of variance .

Variance also indicates a relationship between the mean of a distribution and the data points; it is determined by averaging the sum of the squared deviations. Squaring the differences instead of taking the absolute values allows for greater flexibility in calculating further algebraic manipulations of the data. Another measure of variation is the standard deviation .

Standard deviation is the square root of the variance. This calculation is useful because it allows for the same flexibility as variance regarding further calculations and yet also expresses variation in the same units as the original measurements (Hinkle, Wiersma and Jurs 1988).

Analyzing Differences Between Groups

Statistical tests can be used to analyze differences in the scores of two or more groups. The following statistical tests are commonly used to analyze differences between groups:

A t-test is used to determine if the scores of two groups differ on a single variable. A t-test is designed to test for the differences in mean scores. For instance, you could use a t-test to determine whether writing ability differs among students in two classrooms.

Note: A t-test is appropriate only when looking at paired data. It is useful in analyzing scores of two groups of participants on a particular variable or in analyzing scores of a single group of participants on two variables.

Matched Pairs T-Test

This type of t-test could be used to determine if the scores of the same participants in a study differ under different conditions. For instance, this sort of t-test could be used to determine if people write better essays after taking a writing class than they did before taking the writing class.

Analysis of Variance (ANOVA)

The ANOVA (analysis of variance) is a statistical test which makes a single, overall decision as to whether a significant difference is present among three or more sample means (Levin 484). An ANOVA is similar to a t-test. However, the ANOVA can also test multiple groups to see if they differ on one or more variables. The ANOVA can be used to test between-groups and within-groups differences. There are two types of ANOVAs:

One-Way ANOVA: This tests a group or groups to determine if there are differences on a single set of scores. For instance, a one-way ANOVA could determine whether freshmen, sophomores, juniors, and seniors differed in their reading ability.

Multiple ANOVA (MANOVA): This tests a group or groups to determine if there are differences on two or more variables. For instance, a MANOVA could determine whether freshmen, sophomores, juniors, and seniors differed in reading ability and whether those differences were reflected by gender. In this case, a researcher could determine (1) whether reading ability differed across class levels, (2) whether reading ability differed across gender, and (3) whether there was an interaction between class level and gender.

Analyzing Relationships Among Variables

Statistical relationships between variables rely on notions of correlation and regression. These two concepts aim to describe the ways in which variables relate to one another:

Correlation

Correlation tests are used to determine how strongly the scores of two variables are associated or correlated with each other. A researcher might want to know, for instance, whether a correlation exists between students' writing placement examination scores and their scores on a standardized test such as the ACT or SAT. Correlation is measured using values between +1.0 and -1.0. Correlations close to 0 indicate little or no relationship between two variables, while correlations close to +1.0 (or -1.0) indicate strong positive (or negative) relationships (Hayes et al. 554).

Correlation denotes positive or negative association between variables in a study. Two variables are positively associated when larger values of one tend to be accompanied by larger values of the other. The variables are negatively associated when larger values of one tend to be accompanied by smaller values of the other (Moore 208).

An example of a strong positive correlation would be the correlation between age and job experience. Typically, the longer people are alive, the more job experience they might have.

An example of a strong negative relationship might occur between the strength of people's party affiliations and their willingness to vote for a candidate from different parties. In many elections, Democrats are unlikely to vote for Republicans, and vice versa.

Regression analysis attempts to determine the best "fit" between two or more variables. The independent variable in a regression analysis is a continuous variable, and thus allows you to determine how one or more independent variables predict the values of a dependent variable.

Simple Linear Regression is the simplest form of regression. Like a correlation, it determines the extent to which one independent variables predicts a dependent variable. You can think of a simple linear regression as a correlation line. Regression analysis provides you with more information than correlation does, however. It tells you how well the line "fits" the data. That is, it tells you how closely the line comes to all of your data points. The line in the figure indicates the regression line drawn to find the best fit among a set of data points. Each dot represents a person and the axes indicate the amount of job experience and the age of that person. The dotted lines indicate the distance from the regression line. A smaller total distance indicates a better fit. Some of the information provided in a regression analysis, as a result, indicates the slope of the regression line, the R value (or correlation), and the strength of the fit (an indication of the extent to which the line can account for variations among the data points).

Multiple Linear Regression allows one to determine how well multiple independent variables predict the value of a dependent variable. A researcher might examine, for instance, how well age and experience predict a person's salary. The interesting thing here is that one would no longer be dealing with a regression "line." Instead, since the study deals with three dimensions (age, experience, and salary), it would be dealing with a plane, that is, with a two-dimensional figure. If a fourth variable was added to the equations, one would be dealing with a three-dimensional figure, and so on.

Misuses of Statistics

Statistics consists of tests used to analyze data. These tests provide an analytic framework within which researchers can pursue their research questions. This framework provides one way of working with observable information. Like other analytic frameworks, statistical tests can be misused, resulting in potential misinterpretation and misrepresentation. Researchers decide which research questions to ask, which groups to study, how those groups should be divided, which variables to focus upon, and how best to categorize and measure such variables. The point is that researchers retain the ability to manipulate any study even as they decide what to study and how to study it.

Potential Misuses:

• Manipulating scale to change the appearance of the distribution of data
• Eliminating high/low scores for more coherent presentation
• Inappropriately focusing on certain variables to the exclusion of other variables
• Presenting correlation as causation

Measures Against Potential Misuses:

• Testing for reliability and validity
• Testing for statistical significance
• Critically reading statistics

Annotated Bibliography

Dear, K. (1997, August 28). SurfStat australia . Available: http://surfstat.newcastle.edu.au/surfstat/main/surfstat-main.html

A comprehensive site contain an online textbook, links together statistics sites, exercises, and a hotlist for Java applets.

de Leeuw, J. (1997, May 13 ). Statistics: The study of stability in variation . Available: http://www.stat.ucla.edu/textbook/ [1997, December 8].

An online textbook providing discussions specifically regarding variability.

Ewen, R.B. (1988). The workbook for introductory statistics for the behavioral sciences. Orlando, FL: Harcourt Brace Jovanovich.

A workbook providing sample problems typical of the statistical applications in social sciences.

Glass, G. (1996, August 26). COE 502: Introduction to quantitative methods . Available: http://seamonkey.ed.asu.edu/~gene/502/home.html

Outline of a basic statistics course in the college of education at Arizona State University, including a list of statistic resources on the Internet and access to online programs using forms and PERL to analyze data.

Hartwig, F., Dearing, B.E. (1979). Exploratory data analysis . Newberry Park, CA: Sage Publications, Inc.

Hayes, J. R., Young, R.E., Matchett, M.L., McCaffrey, M., Cochran, C., and Hajduk, T., eds. (1992). Reading empirical research studies: The rhetoric of research . Hillsdale, NJ: Lawrence Erlbaum Associates.

A text focusing on the language of research. Topics vary from "Communicating with Low-Literate Adults" to "Reporting on Journalists."

Hinkle, Dennis E., Wiersma, W. and Jurs, S.G. (1988). Applied statistics for the behavioral sciences . Boston: Houghton.

This is an introductory text book on statistics. Each of 22 chapters includes a summary, sample exercises and highlighted main points. The book also includes an index by subject.

Kleinbaum, David G., Kupper, L.L. and Muller K.E. Applied regression analysis and other multivariable methods 2nd ed . Boston: PWS-KENT Publishing Company.

An introductory text with emphasis on statistical analyses. Chapters contain exercises.

Kolstoe, R.H. (1969). Introduction to statistics for the behavioral sciences . Homewood, ILL: Dorsey.

Though more than 25-years-old, this textbook uses concise chapters to explain many essential statistical concepts. Information is organized in a simple and straightforward manner.

Levin, J., and James, A.F. (1991). Elementary statistics in social research, 5th ed . New York: HarperCollins.

This textbook presents statistics in three major sections: Description, From Description to Decision Making and Decision Making. The first chapter underlies reasons for using statistics in social research. Subsequent chapters detail the process of conducting and presenting statistics.

Liebetrau, A.M. (1983). Measures of association . Newberry Park, CA: Sage Publications, Inc.

Mendenhall, W.(1975). Introduction to probability and statistics, 4th ed. North Scltuate, MA: Duxbury Press.

An introductory textbook. A good overview of statistics. Includes clear definitions and exercises.

Moore, David S. (1979). Statistics: Concepts and controversies , 2nd ed . New York: W. H. Freeman and Company.

Introductory text. Basic overview of statistical concepts. Includes discussions of concrete applications such as opinion polls and Consumer Price Index.

Mosier, C.T. (1997). MG284 Statistics I - notes. Available: http://phoenix.som.clarkson.edu/~cmosier/statistics/main/outline/index.html

Explanations of fundamental statistical concepts.

Newton, H.J., Carrol, J.H., Wang, N., & Whiting, D.(1996, Fall). Statistics 30X class notes. Available: http://stat.tamu.edu/stat30x/trydouble2.html [1997, December 10].

This site contains a hyperlinked list of very comprehensive course notes from and introductory statistics class. A large variety of statistical concepts are covered.

Runyon, R.P., and Haber, A. (1976). Fundamentals of behavioral statistics , 3rd ed . Reading, MA: Addison-Wesley Publishing Company.

This is a textbook that divides statistics into categories of descriptive statistics and inferential statistics. It presents statistical procedures primarily through examples. This book includes sectional reviews, reviews of basic mathematics and also a glossary of symbols common to statistics.

Schoeninger, D.W. and Insko, C.A. (1971). Introductory statistics for the behavioral sciences . Boston: Allyn and Bacon, Inc.

An introductory text including discussions of correlation, probability, distribution, and variance. Includes statistical tables in the appendices.

Stevens, J. (1986). Applied multivariate statistics for the social sciences . Hillsdale, NJ: Lawrence Erlbaum Associates.

Stockberger, D. W. (1996). Introductory statistics: Concepts, models and applications . Available: http://www.psychstat.smsu.edu/ [1997, December 8].

Describes various statistical analyses. Includes statistical tables in the appendix.

Local Resources

If you are a member of the Colorado State University community and seek more in-depth help with analyzing data from your research (e.g., from an undergraduate or graduate research project), please contact CSU's Graybill Statistical Laboratory for statistical consulting assistance at http://www.stat.colostate.edu/statlab.html .

Jackson, Shawna, Karen Marcus, Cara McDonald, Timothy Wehner, & Mike Palmquist. (2005). Statistics: An Introduction. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=67

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Types of Statistics

Statistics involves the use of mathematical methods and techniques to extract meaning and insights from data. There are two types of Statistics; descriptive statistics and inferential statistics.

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. Here we have shared the types of statistics in this article.

Statistics can be broadly categorized into two main types.

• Descriptive statistics
• Inferential statistics

1. Descriptive Statistics

Descriptive statistics is a branch of statistics that deals with the collection, analysis, and presentation of data in a way that allows for a better understanding of its characteristics. The goal of descriptive statistics is to summarize and describe a set of data, rather than drawing inferences or conclusions about a larger population based on the data.

Measures of Central Tendency

Mean, median, and mode are common measures of central tendency used in statistics to describe the typical or central value of a set of data.

The mean is the sum of all the values in a dataset divided by the total number of values. It is also called the arithmetic mean. The mean is sensitive to extreme values, which can skew the value of the mean. For example, in a dataset of test scores, one or two very high or very low scores can significantly affect the mean score.

The median is the middle value in a dataset, such that half of the values are above it and half are below it. The median is less sensitive to extreme values than the mean. For example, in a dataset of test scores, the median score will be unaffected by one or two very high or very low scores.

The mode is the value that appears most frequently in a dataset. It is the only measure of central tendency that can be used for nominal data (data that cannot be ordered or ranked). For example, in a dataset of favorite colors, the mode would be the color that appears most frequently.

All three measures of central tendency have their strengths and weaknesses and are useful in different contexts. In general, the choice of which measure to use depends on the type of data, the purpose of the analysis, and the specific question being asked.

Measures of Variability

Range, variance, and dispersion are measures of variability used in statistics to describe how spread out the values in a dataset are around the central tendency.

The range is the difference between the largest and smallest values in a dataset. It is a simple measure of variability that is easy to calculate, but it is sensitive to extreme values and may not provide a complete picture of the variability in the data.

Variance is a more robust measure of variability that takes into account all of the values in a dataset. It measures how far the values in the dataset are from the mean. A high variance indicates that the values are widely spread out from the mean, while a low variance indicates that the values are clustered closely around the mean.

Standard deviation is the square root of the variance and provides a measure of the spread of the data in the same units as the data.

Dispersion is a general term that refers to how spread out the values in a dataset are around the central tendency. It can be measured using range, variance, or standard deviation.

In general, measures of variability are important in statistical analysis because they provide information about the range and spread of the data, which can affect the conclusions that can be drawn from the data.

Other commonly used descriptive statistics include frequency distributions, which show how often each value or category appears in a dataset, and graphical representations of data such as histograms, scatterplots, and box plots.

Descriptive statistics can be used in a wide range of fields, including business, finance, healthcare, social sciences, and many others.

By analyzing and summarizing data using descriptive statistics, researchers and practitioners can gain insights into the underlying patterns and trends in the data, which can inform decision-making and improve understanding of complex phenomena.

2. Inferential Statistics

Inferential statistics is a branch of statistics that uses sample data to make inferences or draw conclusions about a larger population. The goal of inferential statistics is to use a sample of data to make predictions or generalize findings to the larger population from which the sample was drawn.

Inferential statistics relies on probability theory and hypothesis testing to make inferences about population parameters based on sample statistics. For example, if a researcher wants to estimate the mean income of a population, they might take a sample of individuals from that population and use inferential statistics to estimate the population mean based on the sample mean and other sample statistics.

Some common methods used in inferential statistics include hypothesis testing, confidence intervals, and regression analysis. Hypothesis testing involves making a statement about a population parameter, such as the population means, and then using sample data to determine whether the statement is likely to be true or false. Confidence intervals provide a range of values within which the population parameter is likely to fall with a certain degree of confidence. Regression analysis is a statistical method used to examine the relationship between two or more variables.

Inferential statistics is used in a wide range of fields, including business, healthcare, social sciences, and many others. By drawing inferences and making predictions about population parameters based on sample data, researchers and practitioners can gain insights into complex phenomena and make informed decisions.

Related Posts

• Scope of Statistics
• Importance of Statistics
• Limitations of Statistics

Other Types of Statistics

In addition to these two main types of statistics, there are several other types that are often used in various fields, including:

• Probability Theory
• Biostatistics
• Social Statistics
• Business Statistics

Probability Theory : Probability theory is a branch of mathematics that deals with the study of random events and their outcomes. It provides a framework for understanding and quantifying uncertainty and is used in many fields, including finance, engineering, and physics.

Biostatistics : Biostatistics is a branch of statistics that deals with the application of statistical methods to the study of biological and health-related data. It is used to analyze and interpret data related to diseases, medical treatments, and public health.

Social Statistics : Social statistics is a branch of statistics that deals with the analysis of social data, such as data related to social behavior, public opinion, and demographic characteristics. It is used in fields such as sociology, political science, and psychology.

Business Statistics : Business statistics is a branch of statistics that deals with the analysis and interpretation of data related to business operations, such as sales, profits, and customer behavior. It is used to make informed business decisions, such as setting prices, identifying market trends, and forecasting future performance.

Overall, statistics is a diverse field with many applications, and the different types of statistics offer various techniques and tools for analyzing and interpreting data in different contexts.

Introductory essay

Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.

The reality of today

All of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information...And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. David McCandless

In today's complex 'information jungle,' David McCandless observes that "Data is the new soil." McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource providing a fertile and creative medium from which new ideas and understanding can grow. McCandless's inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his compelling image of flowers growing out of data/soil. These 'flowers' represent the many insights that can be gleaned from effective visualization of data.

We're just learning how to till this soil and make sense of the mountains of data constantly being generated. As Gary King, Director of Harvard's Institute for Quantitative Social Science says in his New York Times article "The Age of Big Data":

It's a revolution. We're really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.

How do we deal with all this data without getting information overload? How do we use data to gain real insight into the world? Finding ways to pull interesting information out of data can be very rewarding, both personally and professionally. The managing editor of Financial Times observed on CNN's Your Money : "The people who are able to in a sophisticated and practical way analyze that data are going to have terrific jobs." Those who learn how to present data in effective ways will be valuable in every field.

Many people, when they think of data, think of tables filled with numbers. But this long-held notion is eroding. Today, we're generating streams of data that are often too complex to be presented in a simple "table." In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses audio, video, and the text messages in social media as data.

Some may also think that only a few specialized professionals can draw insights from data. When we look at data in the right way, however, the results can be fun, insightful, even whimsical — and accessible to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on any other day of the week, or that the most break-ups (at least those discussed on Facebook) occur in mid-December? David McCandless discovered this by analyzing thousands of Facebook status updates.

Data, data, everywhere

There is more data available to us now than we can possibly process. Every minute , Internet users add the following to the big data pool (i):

• 204,166,667 email messages sent
• More than 2,000,000 Google searches
• 684,478 pieces of content added on Facebook
• $272,070 spent by consumers via online shopping
• More than 100,000 tweets on Twitter
• 47,000 app downloads from Apple
• 34,722 "likes" on Facebook for different brands and organizations
• 27,778 new posts on Tumblr blogs
• 3,600 new photos on Instagram
• 3,125 new photos on Flickr
• 2,083 check-ins on Foursquare
• 571 new websites created
• 347 new blog posts published on Wordpress
• 217 new mobile web users
• 48 hours of new video on YouTube

These numbers are almost certainly higher now, as you read this. And this just describes a small piece of the data being generated and stored by humanity. We're all leaving data trails — not just on the Internet, but in everything we do. This includes reams of financial data (from credit cards, businesses, and Wall Street), demographic data on the world's populations, meteorological data on weather and the environment, retail sales data that records everything we buy, nutritional data on food and restaurants, sports data of all types, and so on.

Governments are using data to search for terrorist plots, retailers are using it to maximize marketing strategies, and health organizations are using it to track outbreaks of the flu. But did you ever think of collecting data on every minute of your child's life? That's precisely what Deb Roy did. He recorded 90,000 hours of video and 140,000 hours of audio during his son's first years. That's a lot of data! He and his colleagues are using the data to understand how children learn language, and they're now extending this work to analyze publicly available conversations on social media, allowing them to take "the real-time pulse of a nation."

Data can provide us with new and deeper insight into our world. It can help break stereotypes and build understanding. But the sheer quantity of data, even in just any one small area of interest, is overwhelming. How can we make sense of some of this data in an insightful way?

The power of visualizing data

Visualization can help transform these mountains of data into meaningful information. In his TEDTalk, David McCandless comments that the sense of sight has by far the fastest and biggest bandwidth of any of the five senses. Indeed, about 80% of the information we take in is by eye. Data that seems impenetrable can come alive if presented well in a picture, graph, or even a movie. Hans Rosling tells us that "Students get very excited — and policy-makers and the corporate sector — when they can see the data."

It makes sense that, if we can effectively display data visually, we can make it accessible and understandable to more people. Should we worry, however, that by condensing data into a graph, we are simplifying too much and losing some of the important features of the data? Let's look at a fascinating study conducted by researchers Emre Soyer and Robin Hogarth . The study was conducted on economists, who are certainly no strangers to statistical analysis. Three groups of economists were asked the same question concerning a dataset:

• One group was given the data and a standard statistical analysis of the data; 72% of these economists got the answer wrong.
• Another group was given the data, the statistical analysis, and a graph; still 61% of these economists got the answer wrong.
• A third group was given only the graph, and only 3% got the answer wrong.

Visualizing data can sometimes be less misleading than using the raw numbers and statistics!

What about all the rest of us, who may not be professional economists or statisticians? Nathalie Miebach finds that making art out of data allows people an alternative entry into science. She transforms mountains of weather data into tactile physical structures and musical scores, adding both touch and hearing to the sense of sight to build even greater understanding of data.

Another artist, Chris Jordan, is concerned about our ability to comprehend big numbers. As citizens of an ever-more connected global world, we have an increased need to get useable information from big data — big in terms of the volume of numbers as well as their size. Jordan's art is designed to help us process such numbers, especially numbers that relate to issues of addiction and waste. For example, Jordan notes that the United States has the largest percentage of its population in prison of any country on earth: 2.3 million people in prison in the United States in 2005 and the number continues to rise. Jordan uses art, in this case a super-sized image of 2.3 million prison jumpsuits, to help us see that number and to help us begin to process the societal implications of that single data value. Because our brains can't truly process such a large number, his artwork makes it real.

The role of technology in visualizing data

The TEDTalks in this collection depend to varying degrees on sophisticated technology to gather, store, process, and display data. Handling massive amounts of data (e.g., David McCandless tracking 10,000 changes in Facebook status, Blaise Aguera y Arcas synching thousands of online images of the Notre Dame Cathedral, or Deb Roy searching for individual words in 90,000 hours of video tape) requires cutting-edge computing tools that have been developed specifically to address the challenges of big data. The ability to manipulate color, size, location, motion, and sound to discover and display important features of data in a way that makes it readily accessible to ordinary humans is a challenging task that depends heavily on increasingly sophisticated technology.

The importance of good visualization

There are good ways and bad ways of presenting data. Many examples of outstanding presentations of data are shown in the TEDTalks. However, sometimes visualizations of data can be ineffective or downright misleading. For example, an inappropriate scale might make a relatively small difference look much more substantial than it should be, or an overly complicated display might obfuscate the main relationships in the data. Statistician Kaiser Fung's blog Junk Charts offers many examples of poor representations of data (and some good ones) with descriptions to help the reader understand what makes a graph effective or ineffective. For more examples of both good and bad representations of data, see data visualization architect Andy Kirk's blog at visualisingdata.com . Both consistently have very current examples from up-to-date sources and events.

Creativity, even artistic ability, helps us see data in new ways. Magic happens when interesting data meets effective design: when statistician meets designer (sometimes within the same person). We are fortunate to live in a time when interactive and animated graphs are becoming commonplace, and these tools can be incredibly powerful. Other times, simpler graphs might be more effective. The key is to present data in a way that is visually appealing while allowing the data to speak for itself.

Changing perceptions through data

While graphs and charts can lead to misunderstandings, there is ultimately "truth in numbers." As Steven Levitt and Stephen Dubner say in Freakonomics , "[T]eachers and criminals and real-estate agents may lie, and politicians, and even C.I.A. analysts. But numbers don't." Indeed, consideration of data can often be the easiest way to glean objective insights. Again from Freakonomics : "There is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction."

Data can help us understand the world as it is, not as we believe it to be. As Hans Rosling demonstrates, it's often not ignorance but our preconceived ideas that get in the way of understanding the world as it is. Publicly-available statistics can reshape our world view: Rosling encourages us to "let the dataset change your mindset."

Chris Jordan's powerful images of waste and addiction make us face, rather than deny, the facts. It's easy to hear and then ignore that we use and discard 1 million plastic cups every 6 hours on airline flights alone. When we're confronted with his powerful image, we engage with that fact on an entirely different level (and may never see airline plastic cups in the same way again).

The ability to see data expands our perceptions of the world in ways that we're just beginning to understand. Computer simulations allow us to see how diseases spread, how forest fires might be contained, how terror networks communicate. We gain understanding of these things in ways that were unimaginable only a few decades ago. When Blaise Aguera y Arcas demonstrates Photosynth, we feel as if we're looking at the future. By linking together user-contributed digital images culled from all over the Internet, he creates navigable "immensely rich virtual models of every interesting part of the earth" created from the collective memory of all of us. Deb Roy does somewhat the same thing with language, pulling in publicly available social media feeds to analyze national and global conversation trends.

Roy sums it up with these powerful words: "What's emerging is an ability to see new social structures and dynamics that have previously not been seen. ...The implications here are profound, whether it's for science, for commerce, for government, or perhaps most of all, for us as individuals."

Let's begin with the TEDTalk from David McCandless, a self-described "data detective" who describes how to highlight hidden patterns in data through its artful representation.

David McCandless

The beauty of data visualization.

i. Data obtained June 2012 from “How Much Data Is Created Every Minute?” on http://mashable.com/2012/06/22/data-created-every-minute/ .

Relevant talks

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

Chris Jordan

Turning powerful stats into art.

Blaise Agüera y Arcas

How photosynth can connect the world's images.

The birth of a word

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Home Essay Samples Science

Essay Samples on Statistics

The importance of statistics in daily life: quantifying reality.

Statistics may seem like an abstract field reserved for researchers and analysts, but its influence extends far beyond academic settings. In fact, statistics play a vital role in shaping our daily lives, informing decision-making, guiding policies, and helping us make sense of complex information. From...

Models and Methods of Wind Speed Forecasting

Wind power energy has a significant importance in the electric grid. As it is an inexhaustible and freely available resource, its usage is globally increasing. It is replacing all other costly exhaustible resources that were used so far. So, accurate prediction of wind power is...

Two Types of Statistics: Descriptive and Inferential

Statistics is sub field of both science and mathematics as it uses the rules from these two fields. It works with the calculations and quantifications of data, scrutinizing it, elucidating, and dispensing it in the best form. The students of this subject collect the data...

Introduction To Statistics: Definition, Benefits, Methods

Definition Branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis of their quantifiable likelihood (probability). Statistics can interpret aggregates of data too large to be intelligible by ordinary observation because such data (unlike individual quantities)...

• Qualitative Research

International Business' Statistics of Import and Manufacturing Process

Introduction Cambridge dictionary defines international business as the activity of trading goods and services between countries. However international business is beyond this definition, it has a very wide scope. Basically, international business is a cross border transaction between individuals, business or government entities. The transaction...

• International Business

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The Affects of Demographic Statistics on Human Growth and Other Factors

Demography is the statistical sample of people, particularly in terms of size and density, distribution and vital statistics such as births, marriages, deaths, etc. History has its own ups and downs forever, and demography has been helping us since forever to study the effect of...

• Population Growth

Best topics on Statistics

1. The Importance of Statistics in Daily Life: Quantifying Reality

2. Models and Methods of Wind Speed Forecasting

3. Two Types of Statistics: Descriptive and Inferential

4. Introduction To Statistics: Definition, Benefits, Methods

5. International Business’ Statistics of Import and Manufacturing Process

6. The Affects of Demographic Statistics on Human Growth and Other Factors

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• Albert Einstein
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7.1: Basic Concepts of Statistics

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• Page ID 113176

• David Lippman & Jeff Eldridge
• Pierce College via The OpenTextBookStore

Learning Objectives

• Understand the basic terminology used in statistics
• Understand the difference between populations and samples
• Classify data as categorical or quantitative

Introduction

Like most people, you probably feel that it is important to "take control of your life." But what does this mean? Partly it means being able to properly evaluate the data and claims that bombard you every day. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Statistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, Statistics is one of the most important things that you can study.

To be more specific, here are some claims that we have heard on several occasions. (We are not saying that each one of these claims is true!)

• 4 out of 5 dentists recommend Dentyne.
• Almost 85% of lung cancers in men and 45% in women are tobacco-related.
• Condoms are effective 94% of the time.
• Native Americans are significantly more likely to be hit crossing the streets than are people of other ethnicities.
• People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly.
• Women make 75 cents to every dollar a man makes when they work the same job.
• A surprising new study shows that eating egg whites can increase one's life span.
• People predict that it is very unlikely there will ever be another baseball player with a batting average over 400.
• There is an 80% chance that in a room full of 30 people that at least two people will share the same birthday.
• 79.48% of all statistics are made up on the spot.

All of these claims are statistical in character. We suspect that some of them sound familiar; if not, we bet that you have heard other claims like them. Notice how diverse the examples are; they come from psychology, health, law, sports, business, etc. Indeed, data and data-interpretation show up in discourse from virtually every facet of contemporary life.

Statistics are often presented in an effort to add credibility to an argument or advice. You can see this by paying attention to television advertisements. Many of the numbers thrown about in this way do not represent careful statistical analysis. They can be misleading, and push you into decisions that you might find cause to regret. For these reasons, learning about statistics is a long step towards taking control of your life. (It is not, of course, the only step needed for this purpose.) These chapters will help you learn statistical essentials. It will make you into an intelligent consumer of statistical claims.

You can take the first step right away. To be an intelligent consumer of statistics, your first reflex must be to question the statistics that you encounter. The British Prime Minister Benjamin Disraeli famously said, "There are three kinds of lies -- lies, damned lies, and statistics." This quote reminds us why it is so important to understand statistics. So let us invite you to reform your statistical habits from now on. No longer will you blindly accept numbers or findings. Instead, you will begin to think about the numbers, their sources, and most importantly, the procedures used to generate them.

We have put the emphasis on defending ourselves against fraudulent claims wrapped up as statistics. Just as important as detecting the deceptive use of statistics is the appreciation of the proper use of statistics. You must also learn to recognize statistical evidence that supports a stated conclusion. When a research team is testing a new treatment for a disease, statistics allows them to conclude based on a relatively small trial that there is good evidence their drug is effective. Statistics allowed prosecutors in the 1950’s and 60’s to demonstrate racial bias existed in jury panels. Statistics are all around you, sometimes used well, sometimes not. We must learn how to distinguish the two cases.

Basic Terms

In order to study and understand statistics, you must first be acquainted with the basic terminology.

Data are the individual items of information such as measurements or survey responses that have been collected for a study or analysis.

Statistics is a collection of methods for collecting, displaying, analyzing, and drawing conclusions from data.

There are 2 branches of statistics: descriptive and inferential.

Descriptive Statistics

Descriptive statistics is the branch of statistics that involves collecting, organizing, displaying, and describing data.

Inferential Statistics

Inferential statistics is the branch of statistics that uses probability to analyze, make predictions and draw conclusions based on the data.

We will mainly be exploring descriptive statistics in this class. To learn more about the methods of inferential statistics, you should take a course in introductory statistics.

Before we begin gathering and analyzing data we need to characterize the population we are studying. If we want to study the amount of money spent on textbooks by a typical first-year college student, our population might be all first-year students at your college. Or it might be:

• All first-year community college students in the state of California.
• All first-year students at public colleges and universities in the state of California.
• All first-year students at all colleges and universities in the state of California.
• All first-year students at all colleges and universities in the entire United States.

The population of a study is the group the collected data is intended to describe.

Sometimes the intended population is called the target population , since if we design our study badly, the collected data might not actually be representative of the intended population.

Why is it important to specify the population? We might get different answers to our question as we vary the population we are studying. First-year students at Cal State Fullerton might take slightly more diverse courses than those at your college, and some of these courses may require less popular textbooks that cost more; or, on the other hand, the University Bookstore might have a larger pool of used textbooks, reducing the cost of these books to the students. Whichever the case (and it is likely that some combination of these and other factors are in play), the data we gather from your college will probably not be the same as that from Cal State Fullerton. Particularly when conveying our results to others, we want to be clear about the population we are describing with our data.

Example \(\PageIndex{1}\)

A newspaper website contains a poll asking people their opinion on a recent news article. What is the population?

While the target (intended) population may have been all people, the real population of the survey is readers of the website.

If we were able to gather data on every member of our population, say the average (we will define "average" more carefully in a subsequent section) amount of money spent on textbooks by each first-year student at your college during the 2019-2020 academic year, the resulting number would be called a parameter .

A parameter is a value (average, percentage, etc.) calculated using all the data from a population.

We seldom see parameters, however, since surveying an entire population is usually very time-consuming and expensive, unless the population is very small or we already have the data collected.

A survey of an entire population is called a census .

You are probably familiar with two common censuses: the official government Census that attempts to count the population of the U.S. every ten years, and voting, which asks the opinion of all eligible voters in a district. The first of these demonstrates one additional problem with a census: the difficulty in finding and getting participation from everyone in a large population, which can bias, or skew, the results.

There are occasionally times when a census is appropriate, usually when the population is fairly small. For example, if the manager of Starbucks wanted to know the average number of hours her employees worked last week, she should be able to pull up payroll records or ask each employee directly.

Since surveying an entire population is often impractical, we usually select a sample to study.

A sample is a smaller subset of the entire population, ideally one that is fairly representative of the whole population.

We will discuss sampling methods in greater detail in a later section. For now, let us assume that samples are chosen in an appropriate manner. If we survey a sample, say 100 first-year students at your college, and find the average amount of money spent by these students on textbooks, the resulting number is called a statistic .

A statistic is a value (average, percentage, etc.) calculated using the data from a sample.

Example \(\PageIndex{2}\)

A researcher wanted to know how citizens of Brea felt about a voter initiative. To study this, she goes to the Brea Mall and randomly selects 200 shoppers and asks them their opinion. 60% indicate they are supportive of the initiative. What is the sample and population? Is the 60% value a parameter or a statistic?

The sample is the 200 shoppers questioned. The population is less clear. While the intended population of this survey was Brea citizens, the effective population was mall shoppers. There is no reason to assume that the 200 shoppers questioned would be representative of all Brea citizens.

The 60% value was based on the sample, so it is a statistic.

Try It \(\PageIndex{1}\)

To determine the average length of trout in a lake, researchers catch 20 fish and measure them. What is the sample and population in this study?

The sample is the 20 fish caught. The population is all fish in the lake. The sample may be somewhat unrepresentative of the population since not all fish may be large enough to catch the bait.

Try It \(\PageIndex{2}\)

A college reports that the average age of their students is 28 years old. Is this a statistic or a parameter?

This is a parameter, since the college would have access to data on all students (the population).

Classifying Data

Once we have gathered data, we might wish to classify it. Roughly speaking, data can be classified as categorical data or quantitative data .

Categorical and Quantitative Data

• Categorical (qualitative) data are pieces of information that allow us to classify the objects under investigation into various categories. They are measurements for which there is no natural numerical scale, but which consist of attributes, labels, or other non-numerical characteristics.
• Quantitative data are responses that are numerical in nature and with which we can perform meaningful arithmetic calculations.

Example \(\PageIndex{3}\)

We might conduct a survey to determine the name of the favorite movie that each person in a math class saw in a movie theater. Is the data collected categorical or quantitative?

When we conduct such a survey, the responses would look like: Top Gun: Maverick , Doctor Strange in the Multiverse of Madness , or Turning Red . We might count the number of people who give each answer, but the answers themselves do not have any numerical values: we cannot perform computations with an answer like " Turning Red . " This would be categorical data.

Example \(\PageIndex{4}\)

A survey could ask the number of movies you have seen in a movie theater in the past 12 months (0, 1, 2, 3, 4, ...). Is the data collected categorical or quantitative?

This would be quantitative data since the responses are numerical. We could perform meaningful arithmetic calculations on the data such as finding the average number of movies that people saw in a movie theater in the last year.

Other examples of quantitative data would be the running time of the movie you saw most recently (131 minutes, 126 minutes, 100 minutes, ...) or the amount of money you paid for a movie ticket the last time you went to a movie theater ($10.50, $13.75, $16, ...).

Sometimes, determining whether or not data is categorical or quantitative can be a bit trickier.

Example \(\PageIndex{5}\)

Suppose we gather respondents' ZIP codes in a survey to track their geographical location. Is the data collected categorical or quantitative?

ZIP codes are numbers, but we can't do any meaningful mathematical calculations with them (it doesn't make sense to say that 92806 is "twice" 46403 — that's like saying that Anaheim, CA is "twice" Gary, IN, which doesn't make sense at all), so ZIP codes are really categorical data.

Example \(\PageIndex{6}\)

A survey about the movie you most recently attended includes the question "How would you rate the movie you just saw?" with these possible answers:

1 - It was awful 2 - It was just OK 3 - I liked it 4 - It was great 5 - Best movie ever!

Is the data collected categorical or quantitative?

Again, there are numbers associated with the responses, but we can't really do any calculations with them: a movie that rates a 4 is not necessarily twice as good as a movie that rates a 2, whatever that means; if two people see the movie and one of them thinks it stinks and the other thinks it's the best ever it doesn't necessarily make sense to say that "on average they liked it."

As we study movie-going habits and preferences, we shouldn't forget to specify the population under consideration. If we survey 3-7 year-olds the runaway favorite might be Turning Red . 13-17 year-olds might prefer Doctor Strange . And 33-37 year-olds might prefer Top Gun .

Try It \(\PageIndex{3}\)

Classify each measurement as categorical or quantitative:

• Eye color of a group of people
• Daily high temperature of a city over several weeks
• Annual income
• Categorical
• Quantitative

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How To Write a Statistical Analysis Essay

Home » Videos » How To Write a Statistical Analysis Essay

Statistical analysis is a powerful tool used to draw meaningful insights from data. It can be applied to almost any field and has been used in everything from natural sciences, economics, and sociology to sports analytics and business decisions. Writing a statistical analysis essay requires an understanding of the concepts behind it as well as proficiency with data manipulation techniques.

In this guide, we’ll look at the steps involved in writing a statistical analysis essay. Experts in research paper writing from https://domypaper.me/write-my-research-paper/ give detailed instructions on how to properly conduct a statistical analysis and make valid conclusions.

Overview of statistical analysis essays

A statistical analysis essay is an academic paper that involves analyzing quantitative data and interpreting the results. It is often used in social sciences, economics and business to draw meaningful conclusions from the data. The objective of a statistical analysis essay is to analyze a specific dataset or multiple datasets in order to answer a question or prove or disprove a hypothesis. To achieve this effectively, the information must be analyzed using appropriate statistical techniques such as descriptive statistics, inferential statistics, regression analysis and correlation analysis.

Researching the subject matter

Before writing your statistical analysis essay it is important to research the subject matter thoroughly so that you have an understanding of what you are dealing with. This can include collecting and organizing any relevant data sets as well as researching different types of statistical techniques available for analyzing them. Furthermore, it is important to become familiar with the terminology associated with statistical analysis such as mean, median and mode.

Structuring your statistical analysis essay

The structure of your essay will depend on the type of data you are analyzing and the research question or hypothesis that you are attempting to answer. Generally speaking, it should include an introduction which introduces the research question or hypothesis; a body section which includes an overview of relevant literature; a description of how the data was collected and analyzed and any conclusions drawn from it; and finally a conclusion which summarizes all findings.

Analyzing data and drawing conclusions from it

After collecting and organizing your data, you must analyze it in order to draw meaningful conclusions from it. This involves using appropriate statistical techniques such as descriptive statistics, inferential statistics, regression analysis and correlation analysis. It is important to understand the assumptions made when using each technique in order to analyze the data correctly and draw accurate conclusions from it. When choosing a statistical technique for your research, it is important to consult with an expert https://typemyessay.me/service/research-paper-writing-service who can guide you on the most appropriate approach for your study.

Interpreting results and writing a conclusion

Once you have analyzed the data successfully, you must interpret the results carefully in order to answer your research question or prove/disprove your hypothesis. This involves making sure that any conclusions drawn are soundly based on the evidence presented. After interpreting the results, you should write a conclusion which summarizes all of your findings.

Using sources in your analysis

In order to back up your claims and provide support for your arguments, it is important to use credible sources within your analysis. This could include peer-reviewed articles, journals and books which can provide evidence to support your conclusion. It is also important to cite all sources used in order to avoid plagiarism.

Proofreading and finalizing your work

Once you have written your essay it is important to proofread it carefully before submitting it. This involves checking for grammar, spelling and punctuation errors as well as ensuring that the flow of the essay makes sense. Additionally, make sure that any references cited are correct and up-to-date. If you find it hard to complete your research statistical paper, you may want to consider buying a research paper for sale . This service can save you time and money, allowing you to focus on other important tasks.

Tips for writing a successful statistical analysis essay

Here are some tips for writing a successful statistical analysis essay:

• Research your subject matter thoroughly before writing your essay.
• Structure your paper according to the type of data you are analyzing.
• Analyze your data using appropriate statistical techniques.
• Interpret and draw meaningful conclusions from your results.
• Use credible sources to back up any claims or arguments made.
• Proofread and finalize your work before submitting it.

These tips will help ensure that your essay is well researched, structured correctly and contains accurate information. Following these tips will help you write a successful statistical analysis essay which can be used to answer research questions or prove/disprove hypotheses.

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What Are the 4 Types of Data in Statistics?

04.01.2023 • 4 min read

Sarah Thomas

Subject Matter Expert

This article goes over what types of data in statistics exist, their importance, and several examples.

In This Article

What Is Data?

Types of data in statistics, what is qualitative data, what is quantitative data, faq about data.

In statistics , data are the raw materials we use in our analysis . Data are the pieces of information, such as labels or numbers, we use to test hypotheses and make predictions.

We can divide data into two broad categories: qualitative and quantitative .

We can further divide these two categories into:

Qualit‌ative Data (or Categorical Data)

Qualitative data represent labels or characteristics that divide your overall data set into distinct groups or categories.

Nominal Data

Nominal data are qualitative data with no inherent ranking based on size or magnitude.

Example - Nationality

Ordinal Data

Ordinal data are qualitative data with an order based on size or magnitude.

Example - Self-reported happiness of survey respondents

Very unhappy

Somewhat unhappy

Somewhat happy

Quantitative Data (or Numerical Data)

Quantitative data consists of counts or measurements.

Discrete Data

Discrete data are quantitative data representing distinct countable values. Discrete data usually takes the form of whole numbers or integers.

Example - The number of customers who visit a coffee shop each hour.

Continuous Data

Continuous data are quantitative data where the data can take on decimals or fractional values. They represent measurements. Continuous data can take on an infinite set of values within a range. ‌You usually need a measuring device like a stopwatch or scale to collect continuous data.

Example - The time it takes runners to complete a half-marathon.

The table describes qualitative data and provides some additional examples of the two types of qualitative data: nominal and ordinal

The table describes qualitative data and provides some additional examples of the two types of quantitative data: discrete and continuous .

What are the four different types of data in statistics?

The four main types of statistical data are:

Ordinal data

Nominal data

Discrete data

Continuous data

Ordinal and nominal data both fall under the category of qualitative (or categorical) data. Discrete and continuous data fall under the category of quantitative (or numerical) data.

Why are some numerical categories, such as travel advisory warnings, ordinal rather than discrete?

It’s easy to mistake some types of qualitative data with discrete numerical data. Certain types of qualitative data may take the form of a numerical value, but all that number does is classify your data into groups.

For example, the U.S. State Department issues travel advisory warnings on a scale of 1-4. Where Level 1 urges travelers to exercise some caution while traveling, and Level 4 urges people not to travel to certain destinations. These numbers are discrete values, but all they do is divide travel warnings into categories ranked by severity. The numbers do not count or measure anything.

Is age discrete or continuous?

Age is a good example of a continuous variable that is treated as a discrete variable. Technically, we can measure a person’s age in fractions of a year. This means a person’s age can take on an infinite number of possible values. However, we are typically only concerned with age measured in years; therefore, we treat age as a discrete variable.

What types of data visualizations can I use to represent qualitative and quantitative data?

Certain types of data visualizations, such as bar graphs, pie charts, and frequency tables, are particularly useful for visualizing qualitative data.

Histograms, dot plots, box plots, and frequency distributions are great for visualizing quantitative data.

Are proportions and percentiles qualitative or quantitative data?

Proportions and percentages are quantitative, but they can also bridge qualitative and quantitative data. Percentages allow you to convert qualitative values into quantitative values.

For example, say you have the political affiliation of 1000 voters. The political affiliation (Republican, Democrat, Independent) is a categorical variable , but by taking the proportion of voters who fall into each category, you can convert your data into a numerical variable.

What is the difference between experimental data versus observational data?

In addition to categorizing data as quantitative or qualitative, statisticians and data scientists also categorize data by data collection methods. Statistical data that has been collected through a carefully controlled randomized experiment is called experimental data. Data collected through passive observation are called observational data.

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In this article, we’ll look at medians, one of the fundamental concepts for ordering statistical data. You’ll learn how to calculate them both by hand and with the assistance of statistical software, as well as how to apply them within different statistical contexts.

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Further Reading

What are quartiles statistics 101, understanding variables in statistics: types & examples, what is standard error statistics calculation and overview, what is statistical analysis, parameters vs statistic [with examples], how to find critical value in statistics.

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Descriptive Statistics | Definitions, Types, Examples

Published on 4 November 2022 by Pritha Bhandari . Revised on 9 January 2023.

Descriptive statistics summarise and organise characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population .

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalisable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, frequently asked questions.

There are 3 main types of descriptive statistics:

• The distribution concerns the frequency of each value.
• The central tendency concerns the averages of the values.
• The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

• Go to a library
• Watch a movie at a theater
• Visit a national park

A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarise the frequency of every possible value of a variable in numbers or percentages.

• Simple frequency distribution table
• Grouped frequency distribution table

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Measures of central tendency estimate the center, or average, of a data set. The mean , median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then, the median is the number in the middle. If there are two numbers in the middle, find their mean.

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

• List each score and find their mean.
• Subtract the mean from each score to get the deviation from the mean.
• Square each of these deviations.
• Add up all of the squared deviations.
• Divide the sum of the squared deviations by N – 1.
• Find the square root of the number you found.

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

If you were to only consider the mean as a measure of central tendency, your impression of the ‘middle’ of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to extreme values, you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read ‘across’ the table to see how the independent and dependent variables relate to each other.

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables. It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

Descriptive statistics summarise the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalisable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.
• Univariate statistics summarise only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

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Sources and Types of Data Essay

Validity of classroom and other assessment data, conflicting data, reference list.

Data on its on can be classified into two distinct categories, namely: primary and secondary data. Primary data consists of any and all information collected by a researcher during a study that has not been gathered in the same way, manner or time period by other researchers, studies etc (Lipow & Rosenthal, 1986). In other words, primary data is data unique to that particular study and is usually collected through the use of tailor fit questions meant to find that particular type of information (Lipow & Rosenthal, 1986). Secondary data on the other hand comes from other studies done by individuals, organizations, institutions etc.

It is often the case that in any study secondary data is used to either confirm or backup the primary data collected by a researcher and to help assert the given arguments in the study (Lipow & Rosenthal, 1986). In gathering the two types of data the question of their method of collection or rather their source comes into consideration, sources of primary data are categorized under the observation method, the survey method and the experimental method. Sources for secondary data on the other hand are categorized only under two distinct types, namely: internal and external sources with external sources referring to data from outside of a particular school, organization, institution etc. while internal sources refer those within the organization, school, institution etc.

The assessment of the validity of data falls under 3 distinct types, namely: content, criterion and construct, so long as the data used in a study is able fulfill these particular requirements then it can be considered valid (Llosa, 2008). Under the context of content, this particular method of measurement assesses whether the content of data matches the instructional objectives of the assessment (Llosa, 2008). For the measurement of criterion this examines whether the assessment data is concurrent with external criterion, this can come in the form of whether a particular piece of obtained data from a classroom is concurrent with statewide or national data results under the same category (Llosa, 2008). Lastly, under construct this particular method of measurement determines whether a particular method of assessment actually corresponds to variables as predicted by theory or a previous rationale established during the assessment (Llosa, 2008). So long as classroom and other forms of assessment are able to fulfill these particular methods of measurement then the data can be considered a valid means of measuring student achievement.

It must be noted though that based on the work of Jeong (2010 ) which examined the current performance based system of education prevalent in the U.S. public school system, the current American public system of education has a distinct fault in that in favor of getting students to achieve high test scores as a measurement of academic performance educators are having students memorize formulas, dates and historical events without truly giving them an understanding of why such concepts or events work or happened in that particular way (Jung, 2010). The result is actually an overall reduction in creativity as educators have students become nothing more than robotized automatons who give answers without truly understanding the concepts behind them.

A comparison study done by Zumeta and LaSota (2010) which examined student achievement in private schools in the U.S. noted that students from such institutions had a far greater grasp of the internal concepts of various lessons due to the system encouraging open learning and not necessarily concentrating on performance in tests alone (Zumeta & LaSota, 2010). Taking such an example into consideration it cannot be stated that all methods of data assessment will actually be able to hold true in all cases. While classroom and other methods of data assessment are able to measure how well students are able to give answers they fail to be able to measure how students truly understand internal concepts behind the lessons themselves. Student achievement is not measured by tests alone rather it is determined by how a student is able to internalize and understand a particular lesson or concept beyond merely being able to give an answer when asked (Applegate et al., 2010). It is based on this that while using various methods of data assessment can yield various means of measurement in comparing student performance it cannot be said that they are 100% accurate in determining overall student achievement.

The inherent problem with utilizing conflicting data results is that when quantified into the study the result is varying data sets that serve to confuse rather than enlighten readers as to the findings of a particular study. In assessing whether the leader should emphasize classroom and other types of assessment data it must first be determined whether such data sources are reliable sources of information. In all studies, data utilized and presented should be reliable so as to validate any of the findings and arguments presented, as such they must fulfill the following reliability measurement requirements: test-retests, alternate form and internal consistency. The test-retest method utilizes the same form of assessment twice yet both are given several weeks or months apart in order to judge the reliability of the results, the alternate form test on the other hand is the same as the test-retest method yet varies the items position and content slightly, the final method of measurement is internal consistency which utilizes formulas such as the Kuder-Richardson formula in order to assess the comparative results of both tests. When utilizing such methods it would be best for the leader to utilize data sets which he/she believes are consistent over a particular course of time rather than use two types of data sets with conflicting results.

Applegate, M., Turner, J. D., & Applegate, A. J. (2010). Will the Real Reader Please Stand Up?. Reading Teacher , 63(7), 606-608.

Jung Cheol, S. (2010). Impacts of performance-based accountability on institutional performance in the U.S. Higher Education , 60(1), 47-68.

Lipow, A. G., & Rosenthal, J. A. (1986). The Researcher and the Library: A Partnership in the Near Future. Library Journal , 111(14), 154.

Llosa, L. (2008). Building and Supporting a Validity Argument for a Standards-Based Classroom Assessment of English Proficiency Based on Teacher Judgments. Educational Measurement: Issues & Practice , 27(3), 32-42

Zumeta, W., & LaSota, R. (2010). Recent Patterns in the Growth of Private Higher Education in the United States. ASHE Higher Education Report , 36(3), 91-106.

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Types of Essays: A Comprehensive Guide to Writing Different Essay Types

When it comes to academic writing, essays are one of the most common assignments you will encounter. Essays are a way for you to showcase your understanding of a particular topic, and they come in various forms. Each type of essay has its unique characteristics, and it is essential to understand the differences between them to produce a well-written piece. In this article, we will explore the different types of essays you may encounter in your academic journey.

Types of Essays: Your Ultimate Guide to Essay Writing

Understanding Essays

Definition of essay.

An essay is a piece of writing that presents an argument or a point of view on a particular topic. It is a formal piece of writing that is usually written in the third person and is structured into paragraphs. Essays can be written on a variety of topics, ranging from literature to science, and can be of different lengths. They are often used in academic settings to assess a student’s understanding of a particular subject.

Purpose of Essay

The purpose of an essay is to persuade the reader to accept the writer’s point of view. Essays can be used to argue for or against a particular position, to explain a concept, or to analyze a text. The writer must provide evidence to support their argument and must use persuasive language to convince the reader of their position.

There are four main types of essays: argumentative, expository, narrative, and descriptive. Each type of essay has its own unique characteristics and is written for a different purpose. Understanding the different types of essays is essential for writing a successful essay.

Types of Essays

Narrative essay.

A narrative essay is a type of essay that tells a story. It is often written in the first person point of view, and it can be either fictional or non-fictional. This type of essay allows you to express yourself in a creative and personal way.

When writing a narrative essay, it is important to have a clear and concise thesis statement that sets the tone for the rest of the essay. The thesis statement should be specific and should reflect the main point of the essay. It should also be interesting and engaging to the reader.

One of the key elements of a successful narrative essay is the use of vivid and descriptive language. This helps to create a clear picture in the reader’s mind and makes the story more engaging. Additionally, it is important to use dialogue to bring the characters to life and to show their emotions and personalities.

Another important aspect of a narrative essay is the structure. It should have a clear beginning, middle, and end, and the events should be presented in chronological order. This helps the reader to follow the story and understand the sequence of events.

Descriptive Essay

In a descriptive essay, you are required to describe something, such as an event, a person, a place, a situation, or an object. The primary objective of a descriptive essay is to provide a detailed and vivid description of the topic. By using sensory details, such as sight, sound, touch, smell, and taste, you can create a picture in the reader’s mind and make them feel as if they are experiencing the topic themselves.

When writing a descriptive essay, it is important to choose a topic that you are familiar with and have a personal connection to. This will help you to convey your emotions and feelings effectively and make your essay more engaging and interesting to the reader.

To write a successful descriptive essay, you should follow these steps:

• Choose a topic that you are passionate about and have a personal connection to.
• Brainstorm and create an outline of your essay, including the main points you want to cover and the sensory details you will use.
• Use sensory details to create a vivid and engaging picture in the reader’s mind.
• Use figurative language, such as metaphors and similes, to add depth and complexity to your descriptions.
• Use transitions to connect your ideas and create a smooth flow of information.
• Revise and edit your essay to ensure that it is well-structured, organized, and error-free.

Expository Essay

An expository essay is a type of academic writing that aims to explain, describe, or inform the reader about a particular subject. This type of essay is based on facts, evidence, and examples, and it does not require the writer’s personal opinion or feelings. Expository essays can be written in various styles, including compare and contrast, cause and effect, and problem and solution.

Compare and Contrast Essay

A compare and contrast essay is a type of expository writing that involves comparing and contrasting two or more subjects. This type of essay aims to provide the reader with a better understanding of the similarities and differences between the subjects being compared. To write a successful compare and contrast essay, you need to identify the similarities and differences between the subjects, organize your ideas, and provide supporting evidence.

Cause and Effect Essay

A cause and effect essay is a type of expository writing that explores the causes and consequences of a particular event, situation, or phenomenon. This type of essay aims to explain the reasons behind a particular occurrence and its effects on individuals, society, or the environment. To write a successful cause and effect essay, you need to identify the causes and effects of the subject, organize your ideas, and provide supporting evidence.

Problem and Solution Essay

A problem and solution essay is a type of expository writing that focuses on a particular problem and proposes a solution to it. This type of essay aims to inform the reader about a particular issue and provide a viable solution to it. To write a successful problem and solution essay, you need to identify the problem, explain its causes, propose a solution, and provide supporting evidence.

Persuasive Essay

A persuasive essay is a type of academic writing that aims to persuade the reader to accept the writer’s point of view. In this type of essay, the writer presents their argument and supports it with evidence and reasoning to convince the reader to take action or believe in a particular idea.

To write a persuasive essay, you must first choose a topic that you are passionate about and can argue convincingly. Then, you need to research the topic thoroughly and gather evidence to support your argument. You should also consider the opposing viewpoint and address it in your essay to strengthen your argument.

The structure of a persuasive essay is similar to that of other types of essays. It consists of an introduction, body paragraphs, and a conclusion. In the introduction, you should grab the reader’s attention and clearly state your thesis statement. The body paragraphs should present your argument and evidence, and the conclusion should summarize your argument and restate your thesis statement.

To make your persuasive essay more effective, you can use various persuasive writing strategies, such as appealing to the reader’s emotions, using rhetorical questions, and using vivid language. You can also use statistics, facts, and examples to support your argument and make it more convincing.

Argumentative Essay

An argumentative essay is a type of essay that requires you to present a well-researched and evidence-based argument on a particular topic. The aim of this essay is to convince the reader of your stance on the topic by using logical reasoning and factual evidence.

To write an effective argumentative essay, it is important to have a clear and concise thesis statement that presents your position on the topic. This statement should be supported by strong evidence, such as quotations, statistics, and expert opinions. It is also important to consider and address potential counterarguments to your position.

One key aspect of an argumentative essay is the use of logical fallacies. These are errors in reasoning that can weaken your argument and make it less convincing. Some common logical fallacies include ad hominem attacks, false dichotomies, and straw man arguments. It is important to avoid these fallacies and instead rely on sound reasoning and evidence to support your argument.

When writing an argumentative essay, it is also important to consider your audience. Your tone and language should be appropriate for your intended audience, and you should anticipate and address any potential objections or concerns they may have about your argument.

Analytical Essay

An analytical essay is a type of academic writing that involves breaking down a complex topic or idea into smaller parts to examine it thoroughly. The purpose of this essay is to provide a detailed analysis of a particular subject and to present an argument based on the evidence gathered during the research.

When writing an analytical essay, it is crucial to have a clear thesis statement that outlines the main argument of the essay. The thesis statement should be specific and concise, and it should be supported by evidence from primary and secondary sources.

To write an effective analytical essay, you should follow these steps:

• Choose a topic that interests you and that you can research thoroughly.
• Conduct research to gather relevant information and evidence to support your thesis statement.
• Create an outline to organize your ideas and arguments.
• Write an introduction that provides background information on the topic and presents your thesis statement.
• Develop body paragraphs that provide evidence to support your thesis statement.
• Write a conclusion that summarizes your main points and restates your thesis statement.

When writing an analytical essay, it is important to focus on the analysis rather than just summarizing the information. You should critically evaluate the evidence and present your own interpretation of the data.

Critical Essay

A critical essay is a type of academic writing that involves analyzing, interpreting, and evaluating a text. In a critical essay, you must make a claim about how particular ideas or themes are conveyed in a text, and then support that claim with evidence from primary and/or secondary sources.

To write a successful critical essay, you must first read the text carefully and take notes on its main ideas and themes. You should also consider the author’s purpose and audience, as well as any historical or cultural context that may be relevant to the text.

When writing your critical essay, you should follow a clear and logical structure. Begin with an introduction that provides background information on the text and your thesis statement. In the body of your essay, you should provide evidence to support your thesis, using quotes and examples from the text as well as other sources.

It is important to be critical in your analysis, examining the text in detail and considering its strengths and weaknesses. You should also consider alternative interpretations and counterarguments, and address them in your essay.

Reflective Essay

A reflective essay is a type of academic essay that requires you to analyze and interpret an academic text, such as an essay, a book, or an article. Unlike a personal experience essay, a reflective essay involves critical thinking and evaluation of the material.

In a reflective essay, you are expected to reflect on your own learning and experiences related to the material. This type of essay requires you to think deeply about the material and analyze how it relates to your own experiences and knowledge.

To write a successful reflective essay, you should follow these steps:

• Choose a topic that is relevant to the material you are reflecting on.
• Analyze the material and identify key themes and concepts.
• Reflect on your own experiences and knowledge related to the material.
• Evaluate and analyze the material and your own experiences to draw conclusions and insights.
• Write a clear and concise essay that effectively communicates your reflections and insights.

Remember that a reflective essay is not just a summary of the material, but rather an analysis and evaluation of it. Use examples and evidence to support your reflections and insights, and be sure to use proper citation and referencing to acknowledge the sources of your information.

Personal Essay

A personal essay is a type of essay that involves telling a story about yourself, your experiences, or your feelings. It is often written in the first person point of view and can be a powerful way to share your unique perspective with others.

Personal essays can be used for a variety of purposes, such as college admissions, scholarship applications, or simply to share your thoughts and experiences with a wider audience. They can cover a wide range of topics, from personal struggles and triumphs to reflections on important life events.

When writing a personal essay, it is important to keep in mind that you are telling a story. This means that you should focus on creating a narrative that is engaging and compelling for your readers. You should also be honest and authentic in your writing, sharing your true thoughts and feelings with your audience.

To make your personal essay even more effective, consider incorporating descriptive language, vivid imagery, and sensory details. This can help bring your story to life and make it more memorable for your readers.

Synthesis Essay

A synthesis essay is a type of essay that requires you to combine information from multiple sources to create a cohesive argument. This type of essay is often used in academic writing and requires you to analyze, interpret, and evaluate information from various sources to support your thesis statement.

There are two main types of synthesis essays: explanatory and argumentative. An explanatory synthesis essay aims to explain a particular topic or issue by using different sources to provide a comprehensive overview. On the other hand, an argumentative synthesis essay requires you to take a stance on a particular issue and use evidence from multiple sources to support your argument.

When writing a synthesis essay, it is important to carefully analyze and interpret each source to ensure that the information you are using is relevant and accurate. You should also consider the credibility of each source and evaluate the author’s bias or perspective.

To effectively write a synthesis essay, you should follow a clear structure that includes an introduction, body paragraphs, and a conclusion. The introduction should provide background information on the topic and include a clear thesis statement. The body paragraphs should each focus on a specific aspect of the topic and provide evidence from multiple sources to support your argument. The conclusion should summarize your main points and restate your thesis statement.

Review Essay

A review essay is a type of academic writing that involves analyzing and evaluating a piece of work, such as a book, movie, or article. This type of essay requires you to provide a critical assessment of the work, highlighting its strengths and weaknesses. A successful review essay should provide the reader with a clear understanding of the work being reviewed and your opinion of it.

When writing a review essay, it is important to keep in mind the following guidelines:

• Length: A review essay should be between 1,000 and 1,500 words. This length allows for a thorough analysis of the text without becoming bogged down in details. Of course, the specific length will vary depending on the nature of the text being reviewed and the desired focus of the essay.
• Structure: A review essay should follow a clear and logical structure. Start with an introduction that provides some background information on the work being reviewed and your thesis statement. The body of the essay should provide a summary of the work and a critical analysis of its strengths and weaknesses. Finally, end with a conclusion that summarizes your main points and provides your final thoughts on the work.
• Evidence: A successful review essay should be supported by evidence from the work being reviewed. This can include direct quotes or paraphrases, as well as examples that illustrate your points.
• Critical Thinking: A review essay requires you to engage in critical thinking. This means that you must evaluate the work being reviewed in a thoughtful and analytical manner, considering both its strengths and weaknesses.

Research Essay

When it comes to writing a research essay, you must conduct in-depth independent research and provide analysis, interpretation, and argument based on your findings. This type of essay requires extensive research, critical thinking, source evaluation, organization, and composition.

To write a successful research essay, you must follow a specific structure. Here are some key components to include:

Introduction

The introduction should provide a brief overview of your research topic and state your thesis statement. Your thesis statement should clearly state your argument and the main points you will cover in your essay.

Literature Review

The literature review is a critical analysis of the existing research on your topic. It should provide a summary of the relevant literature, identify gaps in the research, and highlight the significance of your study.

Methodology

The methodology section should describe the methods you used to conduct your research. This may include data collection methods, sample size, and any limitations of your study.

The results section should present your findings in a clear and concise manner. You may use tables, graphs, or other visual aids to help convey your results.

The discussion section should interpret your results and provide a critical analysis of your findings. You should also discuss the implications of your research and how it contributes to the existing literature on your topic.

The conclusion should summarize your main findings and restate your thesis statement. You should also discuss the limitations of your study and suggest avenues for future research.

Report Essay

A report essay is a type of essay that presents and summarizes factual information about a particular topic, event, or issue. The purpose of a report essay is to provide readers with a clear and concise understanding of the subject matter. It is important to note that a report essay is not an opinion piece, but rather a neutral presentation of facts.

When writing a report essay, it is important to follow a structured format. The typical format includes an introduction, body, and conclusion. The introduction should provide background information on the topic and state the purpose of the report. The body should present the facts in a logical and organized manner, using headings and subheadings to help readers navigate the information. The conclusion should summarize the key findings and provide any recommendations or conclusions.

One of the key elements of a report essay is research. It is essential to conduct thorough research on the topic to ensure that the information presented is accurate and reliable. This may involve reviewing academic articles, government reports, and other sources of information. It is also important to cite all sources used in the report essay using a recognized citation style, such as APA or MLA.

Informal Essay

An informal essay, also known as a familiar or personal essay, is a type of essay that is written in a personal tone and style. This type of essay is often written as a reflection or commentary on a personal experience, opinion, or observation. Informal essays are usually shorter than formal essays and are often written in a conversational style.

In an informal essay, you are free to use first-person pronouns and to express your personal opinions and feelings. However, you should still strive to maintain a clear and concise writing style and to support your arguments with evidence and examples.

Informal essays can take many forms, including personal narratives, anecdotes, and reflections on current events or social issues. They can also be humorous or satirical in nature, and may include elements of fiction or creative writing.

When writing an informal essay, it is important to keep your audience in mind and to use language and examples that will be familiar and relatable to them. You should also be aware of your tone and style, and strive to create a voice that is engaging and authentic.

Short Essay

When it comes to writing a short essay, it is essential to convey your thoughts and ideas in a concise and clear manner. Short essays are usually assigned in the range of 250-750 words, and occasionally up to 1,000 words. Therefore, it is important to focus on the most important elements of your topic.

To write a successful short essay, you should start by selecting a topic that is interesting and relevant. Once you have chosen your topic, you should conduct thorough research to gather evidence and support for your argument. This will help you to develop a clear and concise thesis statement.

When writing your short essay, it is important to structure your ideas in a logical and coherent manner. You should start with an introduction that provides background information and a clear thesis statement. The body of your essay should be structured around your main points, with each paragraph focusing on a specific idea or argument. Finally, you should conclude your essay by summarizing your main points and restating your thesis statement.

To make your short essay more engaging and impactful, you may want to consider using bullet points, tables, and other formatting techniques to convey your ideas more clearly. Additionally, you should use strong and clear language, avoiding jargon and unnecessary words.

When it comes to academic writing, a long essay is a common type of assignment that you may encounter. This type of essay typically requires you to conduct extensive research and analysis on a specific topic.

The length of a long essay can vary depending on the assignment requirements, but it is usually longer than a standard essay. In general, a long essay can range from 2,500 to 5,000 words or more.

To write a successful long essay, it is important to have a clear understanding of the topic and to conduct thorough research. This may involve reading academic articles, books, and other sources to gather information and support your arguments.

In addition to research, a long essay should also have a clear and well-structured argument. This may involve outlining your main points and supporting evidence, as well as addressing any counterarguments or potential weaknesses in your argument.

Overall, a long essay requires a significant amount of time and effort to complete. However, by following a clear structure and conducting thorough research, you can produce a well-written and persuasive essay that meets the requirements of your assignment.

Some tips for writing a successful long essay include:

• Start early to give yourself enough time to research and write
• Break down the assignment into manageable sections
• Use clear and concise language
• Provide sufficient evidence to support your arguments
• Use proper citation and referencing to avoid plagiarism

Five Paragraph Essay

If you are a student, you have likely been assigned a five-paragraph essay at some point. This type of essay is commonly used in high school and college writing classes. The five-paragraph essay is a structured format that consists of an introduction, three body paragraphs, and a conclusion.

The introduction paragraph is where you present your thesis statement, which is the main idea or argument that you will discuss in your essay. This paragraph should grab the reader’s attention and provide some background information about the topic. It should also include a clear thesis statement that outlines what you will be discussing in the essay.

The three body paragraphs are where you provide evidence to support your thesis statement. Each paragraph should focus on a single point that supports your thesis. You should use specific examples and evidence to back up your claims. Each paragraph should also include a transition sentence that connects it to the next paragraph.

The conclusion paragraph is where you wrap up your essay and restate your thesis statement. This paragraph should summarize the main points of your essay and leave the reader with a clear understanding of your argument. You should avoid introducing any new information in the conclusion paragraph.

Scholarship Essay

A scholarship essay is a crucial document that can help you secure financial aid for your academic pursuits. It is a written statement that highlights your qualifications, accomplishments, and goals. Scholarship essays are typically required by organizations that offer scholarships to students. The essay is meant to help the organization understand why you are deserving of the scholarship and how it will help you achieve your academic and career goals.

To write an effective scholarship essay, it is important to understand the prompt and the organization offering the scholarship. Many scholarship essay prompts are open-ended, which means that you can write about any topic that is relevant to you. However, it is important to ensure that your essay is aligned with the values and goals of the scholarship organization.

When writing a scholarship essay, it is important to be concise and clear. Use simple language and avoid jargon or technical terms that the reader may not understand. Make sure that your essay is well-structured and organized, with a clear introduction, body, and conclusion. Use headings and subheadings to make your essay easy to read and navigate.

To make your scholarship essay stand out, use specific examples and anecdotes that demonstrate your qualifications and accomplishments. Use concrete details and avoid generalizations. Be honest and authentic, and avoid exaggerating or making false claims. Finally, proofread your essay carefully to ensure that it is free of errors and typos.

Frequently Asked Questions

What are the different types of academic essays?

There are four main types of academic essays: argumentative, expository, narrative, and descriptive. Each type has its own unique purpose and structure, and it’s important to understand the differences between them in order to write effectively.

What are the parts of a standard essay?

A standard essay typically consists of three main parts: an introduction, a body, and a conclusion. The introduction should provide background information on the topic and include a thesis statement that outlines the main argument of the essay. The body should present evidence and support for the thesis statement, and the conclusion should summarize the main points and restate the thesis in a new way.

Can you provide examples of different types of essays?

Sure, here are some examples of each type of essay:

• Argumentative: An essay that presents a clear argument on a controversial topic, such as gun control or abortion.
• Expository: An essay that explains or describes a topic, such as how to bake a cake or the history of the Civil War.
• Narrative: An essay that tells a story, such as a personal experience or a fictional tale.
• Descriptive: An essay that uses sensory details to paint a picture of a person, place, or thing, such as a description of a sunset or a character in a novel.

How do you write a narrative essay?

To write a narrative essay, you should first choose a topic that is meaningful to you and has a clear beginning, middle, and end. Then, you should use descriptive language and sensory details to bring the story to life for the reader. Finally, you should reflect on the experience and what you learned from it.

What are the four main types of essays?

The four main types of essays are argumentative, expository, narrative, and descriptive. Each type has its own unique purpose and structure, and it’s important to understand the differences between them in order to write effectively.

What are the three parts of the essay format?

The three parts of the essay format are the introduction, the body, and the conclusion. The introduction should provide background information on the topic and include a thesis statement that outlines the main argument of the essay. The body should present evidence and support for the thesis statement, and the conclusion should summarize the main points and restate the thesis in a new way.

Last Updated on August 31, 2023

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Live IELTS Statistics

The presented data visualisation about topics and question types is updating online as every new IELTS question is added to our database. As all questions are not official (they are reported by exam-takers and gathered from the internet), the statistics based on them could represent an incomplete view; however, due to the huge number of samples, the resulting error is likely minimal, and we can assume the overall picture is accurate.

Let's start with the analysis of topic distribution for the whole exam — both, speaking and writing tasks:

Overall IELTS topic statistics

The pie chart above illustrates real-time cumulative data about IELTS question topics from both parts of the exam: Speaking and Writing. Overall, the exam themes are nicely diversified — there is no dominant theme.

You will notice 7 main topics with around 10% each and three of them could be considered the core themes of IELTS — Education , Entertainment , and Places . With Work and Health and Diet , they hold half of the pie. It simply means there is a 50% probability that you will meet one of these topics in your IELTS exam.

With about 10% each, Storytelling and Life are special task categories, and, in fact, might be about any topic that is not included in other sections. The only difference is that Storytelling includes tasks where you should tell a story (Speaking part 2 or Writing part 1 Letter), rather than describe something or share an opinion.

Nevertheless, if you take a look at particular parts of the IELTS test, you'll see that they are not as consistent:

IELTS subpart topic statistics

Ielts letter (writing task 1) topic data, ielts essay (writing task 2) topic data, ielts speaking topic data.

Okay, now let's look at writing task type distribution:

IELTS Writing Question Types

Ielts letters (writing task 1) question type data.

When it comes to IELTS letters, nearly half of all tasks are of the Giving Information type. In the middle, Request and Complain have about 20% probability each, which means two of five random letter tasks will be from these categories. With a paltry 5% each, four other types — Apology , Invitation , Thanking , and Applying for a Job — win the third place.

IELTS Essays (Writing Task2) question type data

IELTS essay type distribution is more smooth; however, the major type — Argument Essay — also keeps a hefty number (roughly 50%) of all Writing Task 2 questions. The second most frequent type, Opinion Essay , is half as common. The Two-question Essay type represents about 15% of the pie. Finally, 1-in-10 essays are the Problem-and-Solution Essay , which is the rarest type, but it is still significant enough to practice.

Test Type Statistics: Academic IELTS vs General IELTS

Unsurprisingly, in terms of the exam type popularity, Academic IELTS , without a doubt, is the leader — three-fourths of all IELTS-takers go for the Academic type of the exam. General IELTS has the remaining 25%. It is not some shocking fact, because there are more test takers who take the exam for an application to a school or university than for other purposes.

Motivation statistics: reasons for taking IELTS

Charts below were calculated by IELTS officials and published on ielts.org.

There is something interesting in the charts below: the IELTS band distributions for different brackets based on the purpose for taking the test. The purposes are declared by test takers themselves (in a questionnaire that you fill while registering for the exam).

Academic IELTS takers' scores

First of all, people who declared immigration or employment as a purpose have the best score distributions for this type of exam. However, the number of people in these groups should be comparably low, as it is super rare to use the Academic IELTS for a job or immigration application. Meanwhile, IELTS candidates doing the test for educational goals (the most significant group in Academic exam test-takers) have markedly lower scores.

The cut-off IELTS bands for bachelor and master admissions usually lie in a range of 6 to7.5. The graph shows that only half of candidates got the six-and-higher score and about one-fourth had less than 5.5. (It is perhaps also important to remember that when applying for international studies, you may also need a certain IELTS score for your visa, too.)

General IELTS takers' scores

What is markedly noticeable is that General scores are, on average, stronger, and future immigrants and doctors (but not nurses!) perform the best. More than four-fifths of them have a band higher than 6.

Overall, the main facts are:

• Academic IELTS is a bit more difficult — General IELTS band distributions are shifted by nearly 0.5 points towards the higher scores, compared to Academic IELTS.
• Do not even think you receive band 9 in IELTS — statistics says this mission is almost impossible. 
• If you want to get a higher band score, become a dentist and decide to immigrate ! — We can safely assume from this analysis that people who decided to immigrate or want to work as doctors in English-speaking countries are the most motivated candidates and this leads to higher IELTS results.

Congratulations! Now, after reading these insights, IELTS has become clearer for you, and we hope you will use this information to enhance your effectiveness in IELTS preparation with ielts777.com .

IELTS videos (tips, strategies, and mock tests):

Ielts speaking score 8.5 – india — ielts speaking videos, ielts listening tips — ielts preparation videos, improve your ielts speaking in just 60 minutes — ielts preparation videos, 1 simple trick to become fluent in english — ielts preparation videos, canada pr express entry tips from an iccrc member — after-ielts videos, ielts newsletter.

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A global analysis of habitat fragmentation research in reptiles and amphibians: what have we done so far?

• Review Paper
• Open access
• Published: 08 January 2023
• Volume 32 , pages 439–468, ( 2023 )

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• W. C. Tan   ORCID: orcid.org/0000-0002-6067-3528 1 ,
• A. Herrel   ORCID: orcid.org/0000-0003-0991-4434 2 , 3 , 4 &
• D. Rödder   ORCID: orcid.org/0000-0002-6108-1639 1  

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Habitat change and fragmentation are the primary causes of biodiversity loss worldwide. Recent decades have seen a surge of funding, published papers and citations in the field as these threats to biodiversity continue to rise. However, how research directions and agenda are evolving in this field remains poorly understood. In this study, we examined the current state of research on habitat fragmentation (due to agriculture, logging, fragmentation, urbanisation and roads) pertaining to two of the most threatened vertebrate groups, reptiles and amphibians. We did so by conducting a global scale review of geographical and taxonomical trends on the habitat fragmentation types, associated sampling methods and response variables. Our analyses revealed a number of biases with existing research efforts being focused on three continents (e.g., North America, Europe and Australia) and a surplus of studies measuring species richness and abundance. However, we saw a shift in research agenda towards studies utilising technological advancements including genetic and spatial data analyses. Our findings suggest important associations between sampling methods and prevalent response variables but not with the types of habitat fragmentation. These research agendas are found homogeneously distributed across all continents. Increased research investment with appropriate sampling techniques is crucial in biodiversity hotpots such as the tropics where unprecedented threats to herpetofauna exist.

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Introduction

Habitat loss and fragmentation are the predominant causes underlying widespread biodiversity changes in terrestrial ecosystems (Fahrig 2003 ; Newbold et al. 2015 ). These processes may cause population declines by disrupting processes such as dispersal, gene flow, and survival. Over the past 30 years habitat loss and fragmentation have been suggested to have reduced biodiversity by up to 75% in different biomes around the world (Haddad et al. 2015 ). This is mainly due to the clearing of tropical forests, the expansion of agricultural landscapes, the intensification of farmland production, and the expansion of urban areas (FAO and UNEP 2020 ). The rate of deforestation and corresponding land conversions of natural habitats are happening rapidly and will continue to increase in the future at an accelerated rate, particularly in biodiversity hotspots (Deikumah et al. 2014 ; Habel et al. 2019 ; FAO and UNEP 2020 ).

For this reason, habitat fragmentation has been a central research focus for ecologists and conservationists over the past two decades (Fardila et al. 2017 ). However, habitat fragmentation consists of two different processes: loss of habitat and fragmentation of existing habitat (Fahrig 2003 ). The former simply means the removal of habitat, and latter is the transformation of continuous areas into discontinuous patches of a given habitat. In a radical review, Fahrig ( 2003 ) suggested that fragmentation per se, i.e., the breaking up of habitat after controlling for habitat loss, has a weaker or even no effect on biodiversity compared to habitat loss. She further recommended that the effects of these two components should be measured independently (Fahrig 2017 ). Despite being recognised as two different processes, researchers tend not to distinguish between their effects and commonly lump the combined consequences under a single umbrella term “habitat fragmentation” (Fahrig 2003 , 2017 ; Lindenmayer and Fischer 2007 ; Riva and Fahrig 2022 ). Nonetheless, fragmentation has been widely recognised in the literature and describes changes that occur in landscapes, including the loss of habitat (Hadley and Betts 2016 ). Hence, to avoid imprecise or inconsistent use of terminology and provide a holistic view of the effect of modified landscapes, we suggest the term “habitat fragmentation” to indicate any type of landscape change, both habitat loss and fragmentation throughout the current paper.

One main conundrum is that biodiversity decline does not occur homogeneously everywhere nor among all species (Blowes et al. 2019 ). Moreover, we should expect a global disparity in biodiversity responses to habitat fragmentation across different biomes (Newbold et al. 2020 ; Cordier et al. 2021 ). For example, tropical regions are predicted to have higher negative effects of habitat fragmentation than temperate regions. There are two possible reasons: a) higher intensification of land use change in the tropics (Barlow et al. 2018 ), and b) forest animals in the tropics are less likely to cross open areas (Lindell et al. 2007 ). Furthermore, individual species respond to landscape modification differently; some thrive whereas others decline (Fahrig 2003 ). Habitat specialists with broader habitat tolerance and wide-ranging distributions are most likely to benefit from increase landscape heterogeneity and more open and edge habitat (Hamer and McDonnell 2008 ; Newbold et al. 2014 ; Palmeirim et al. 2017 ). Therefore, appropriate response metrics should be used in measuring the effect of habitat fragmentation on biodiversity depending on the taxa group, biome and scale of study as patterns of richness can sometimes be masked by the abundance of generalist species (Riemann et al. 2015 ; Palmeirim et al. 2017 ).

Previous reviews have identified general patterns and responses of reptile and amphibian populations to habitat modification. They have been largely centred around specific types of habitat fragmentation: land use change (Newbold et al. 2020 ), logging (Sodhi et al. 2004 ), fragmentation per se (Fahrig 2017 ), urbanisation (Hamer and McDonnell 2008 ; McDonald et al. 2013 ), fire (Driscoll et al. 2021 ), and roads (Rytwinski and Fahrig 2012 ). Few reviews have, however, attempted a global synthesis of all types of land use changes and even fewer have addressed biases in geographical regions and taxonomical groups (but see Gardner et al. ( 2007 ) and Cordier et al. ( 2021 )). Gardner et al. ( 2007 ) synthesised the extant literature and focused on 112 papers on the consequences of habitat fragmentation on reptiles and amphibians published between 1945 and 2006. They found substantial biases across geographic regions, biomes, types of data collected as well as sampling design and effort. However, failure to report basic statistics by many studies prevented them from performing meta-analyses on research conclusions. More recently, a review by Cordier et al. ( 2021 ) conducted a meta-analysis based on 94 primary studies on the overall effects of land use changes through time and across the globe. Yet, there has been no comprehensive synthesis on the research patterns and agenda of published literature on habitat fragmentation associated with the recent advances of novel research tools and techniques. Therefore, our review may provide new insights of the evolution and biases in the field over the last decades and provide a basis for future research directions. Knowledge gaps caused by these biases could hamper the development of habitat fragmentation research and the implementation of effective strategies for conservation.

We aim to remedy this by examining research patterns for the two vertebrate classes Amphibia and Reptilia, at a global scale. We chose amphibians and reptiles for several reasons. First, habitat fragmentation research has been dominated by birds and mammals (Fardila et al. 2017 ). Reptiles and amphibians, on the other hand, are under-represented; together, they constitute only 10% of the studies (Fardila et al. 2017 ). Second, high proportions of amphibian and reptile species are threatened globally. To date, more than one third of amphibian (40%) and one in five reptile species (21%) are threatened with extinction (Stuart et al. 2004 ; Cox et al. 2022 ). Amphibians are known to be susceptible to land transformation as a result of their cryptic nature, habitat requirements, and reduced dispersal ability (Green 2003 ; Sodhi et al. 2008 ; Ofori‐Boateng et al. 2013 ; Nowakowski et al. 2017 ). Although poorly studied (with one in five species classified as data deficient) (Böhm et al. 2013 ), reptiles face the same threats as those impacting amphibians (Gibbons et al. 2000 ; Todd et al. 2010 ; Cox et al. 2022 ). Reptiles have small distributional ranges with high endemism compared to other vertebrates and as such are likely vulnerable to habitat fragmentation (Todd et al. 2010 ; Meiri et al. 2018 ). Third, both these groups are poikilotherms whose physiology makes them highly dependent on temperature and precipitation levels. Hence, they are very sensitive to changing thermal landscapes (Nowakowski et al. 2017 ).

Here, we first ask how is the published literature distributed across geographic regions and taxa? Is there a bias in the geographic distribution of species studied compared to known species? It is well known that conservation and research efforts are often concentrated in wealthy and English-speaking countries (Fazey et al. 2005 ), but has this bias improved over the years? Second, how are researchers conducting these studies? We assess whether certain sampling methods and response variables are associated to specific types of habitat fragmentation. Over the past decades new tools and techniques are constantly being discovered or developed. Combinations of methodologies are now shedding new light on biodiversity responses and consequences of habitat fragmentation. In particular, genetic techniques are useful in detecting changes in population structure, identifying isolated genetic clusters, and in estimating dispersal (Smith et al. 2016 ). Similarly, habitat occupancy and modelling can also provide powerful insights into dispersal (Driscoll et al. 2014 ). Remote sensing data are now used in analysing effects of area, edge, and isolation (Ray et al. 2002 ). Finally, how are these associations or research agendas distributed across space? We expect to find geographic structure of emerging agendas across the globe. For instance, we predict genetic studies to be located in North America and Europe but also in East Asian countries such as China and Japan as a result of their advancement in genetics (Forero et al. 2016 ). On the other hand, simple biodiversity response indicators which do not require extensive capacity building and application of advanced technologies are likely more used in developing regions of the world (Barber et al. 2014 ). These findings are valuable to evaluate and update the global status of our research on the effects of habitat fragmentation on herpetofauna and to suggest recommendations for conservation plans.

Materials and methods

Data collection.

We conducted the review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Fig.  1 ) (Moher et al. 2009 ). We conducted a comprehensive and exhaustive search using Web of Science to review published studies reporting the consequences of habitat fragmentation on amphibians and reptiles. We consulted the database in November 2019 by using two general search strings: (1) Habitat fragmentation AND (frog* OR amphib* OR salamander* OR tadpole*) (2) Habitat fragmentation AND (reptil* OR snake* OR lizard* OR turtle* OR crocodile*). This returned a total of 869 records from search (1) and 795 from search (2), with 1421 unique records remaining after duplicates were removed. We did not include “habitat loss” in our search term as it would only introduce unrelated articles focusing on biodiversity and conservation management instead of methodology and mechanistic approaches.

PRISMA flow-diagram of the study selection process

Throughout, we use the term papers to refer to individual journal article records. Out of the 1421 papers, we were unfortunately not able to locate seven papers from Acta Oecologica, Zoology: Analysis of Complex Systems, Israel Journal of Ecology and Evolution, Western North American Naturalist, Natural Areas Journal, Ecology, and the Herpetological Journal. We screened all articles from the title through the full text to determine whether they met our criteria for inclusion. To be included, studies needed to fulfil several criteria. First, papers needed to be peer-reviewed journal articles containing data collected on reptiles and/or amphibians at the species level (224 articles rejected because no species-specific data was available). Reviews and metastudies (n = 102) were excluded from the data analysis as they may represent duplicates as they are mainly based on data sets from other papers, but these form an integral part of our discussion. Furthermore, papers which do not provide data on contemporary time scales such as long-term (> 10, 000 years ago) changes on the paleo-spatial patterns (n = 59) were excluded. Because the effects of fragmentation per se have been measured inconsistently by many authors and have not been differentiated from habitat removal (Fahrig 2003 ), we consider any recent anthropogenic habitat degradation, and modification at patch or/and landscape scales during the Holocene as an effect of habitat fragmentation. Only papers which examined direct or indirect effects of habitat fragmentation were included in our analysis, regardless of the magnitude and direction. Papers which did not mention specific types of habitat fragmentation as the focus of their study (n = 338) were excluded.

Geographical and taxonomical distribution

Using the selected papers, we compiled a taxonomic and geographical database for each paper: (a) GPS or georeferenced location of the study site; (b) the focal group investigated (amphibian and/or reptile); (c) taxonomic groups (order, family, genus).

We listed the overall number of species studied covered by selected papers in each continent and compared them to the total number of currently described species. We obtained total described species of both reptiles and amphibians from the following sources: ReptileDatabase (Uetz et al. 2021 ) and AmphibiaWeb (AmphibiaWeb 2021 ). Then, we calculated the proportions of species covered by the selected papers compared to total number of described species for each continent. We did not update species nomenclature from selected papers as the mismatches from these potentially outdated taxonomies would be insignificant in our analyses.

Categorisation of papers

Each paper is classified into three main types of data collected: forms of habitat fragmentation, sampling methods, and response variables (Online Appendix 1). A paper can be classified into one or multiple categories in each type of data. The types of data and their following categories were:

Forms of habitat fragmentation

We recorded different types of habitat fragmentation from the selection of studies: (1) “Fragmentation” (includes patch isolation, edge and area effects); (2) “Agriculture” (includes any form of commercial and subsistence farming such as monocultures, plantations, and livestock farming); (3) “Logging” (e.g., agroforestry and silviculture); (4) “Mining” (presence of mining activities); (5) “Urbanisation” (includes presence of cities, towns or villages and parks created for recreational purposes); (6) “Road” (includes any vehicle roadway such as railways and highways) and (7) “Other types of habitat fragmentation” (e.g., fire, river dams, ditches, diseases, desertification etc.). Many studies deal with more than one type of habitat fragmentation. However, we made sure the selection for fragmentation forms is mainly based on the focus and wordings in the methodology section.

Sampling methods

We report trends in the design and sampling methods among the compiled studies over the last three decades. Due to the substantial variability in the level of sample design information reported by different studies, we narrowed them down into six general categories representing common sampling methods. Common methods used in estimating herpetofauna diversity (e.g., visual transect surveys, acoustic monitoring and trapping methods) were not included in the analyses due to their omnipresence in the data. The categories are:

(1) “Genetics” studies documented any use of codominant markers (i.e., allozymes and microsatellites), dominant markers [i.e., DNA sequences, random amplified polymorphic DNA (RAPDs) and amplified fragment length polymorphism (AFLPs)] to analyse genetic variability and gene diversity respectively. (2) “Direct tracking methods” studies measured potential dispersal distances or species movement patterns by means of radio telemetry, mark-recapture methods, or fluorescent powder tracking. (3) “Aerial photographs” studies reported the use of aerial photographs while (4) “GIS/Satellite image” studies described the use of satellite imagery and land cover data (i.e., Landsat) and GIS programs (e.g., QGIS and ArcGIS, etc.) in analysing spatial variables. (5) “Experimental” studies involved predictions tested through empirical studies, regardless if they occur naturally or artificially; in a natural or a captive environment. (6) “Prediction/simulation models” studies made use of techniques such as ecological niche models, habitat suitability (i.e., occurrence and occupancy models) and simulations for probability of survival and population connectivity.

Response variables

To further conceptualise how the effects of habitat fragmentation are measured, we assigned 12 biodiversity or ecological response variables. We recorded the type of data that was used in all selected studies: (1) “Species richness or diversity” which are measures of species richness, evenness or diversity (such as the Shannon–Wiener index) (Colwell 2009 ); (2) “Functional richness or species guilds” describes diversity indices based on functional traits (such as body size, reproductive modes, microhabitat association or taxonomic groups); (3) “Presence/absence” or species occupancy; (4) “Population” includes an estimation of population size or density (only when measured specifically in the paper). It includes genetic variation and divergence within and between populations; (5) “Abundance” or counts of individuals for comparison between habitat fragmentation type or species; (6) “Dispersal” takes into account any displacement or movement and can include indirect measurements of dispersal using genetic techniques; (7) “Breeding sites” which measures available breeding or reproduction sites; (8) “Fitness measure” are records of any physiological, ecological or behavioural changes; (9) “Interspecific interaction” depicts any interaction between species including competition and predation; (10) “Extinction or colonisation rate” counts the number of population extinctions or colonisations within a time period; (11) “Microhabitat preference” includes any direct observation made on an individual’s surrounding environmental features (substrate type, perch height, vegetation type, distance to cover etc.); (12) “Generalist or specialist comparison” involves any comparison made between generalist and specialist species. Generalists are able to thrive in various environments whereas specialists occupy a much narrower niche; (13) “Other response variables” can include road kill mortality counts, infection rate of diseases, injury, or any effect from introduced animals and a variety of other responses.

Data analysis

All statistical analyses were conducted in the open source statistical software package R 4.1.0 (R Core Team 2021 ). To gain a broad insight into our understanding of the complexity of habitat fragmentation we applied a Multiple Correspondence Analysis (MCA) (Roux and Rouanet 2004 ) and Hierarchical Clustering on Principle Components (HCPC) (Ward 1963 ) to investigate potential interactions between forms of habitat fragmentation, sampling methods and response variables. MCA is ideal for investigation of datasets with multiple categorical variables and exploration of unbiased relationships between these variables.

We first separate the dataset into papers concerning amphibians or reptiles. The MCA was performed using the MCA function from FactoMineR package of R version 3.1 (Lê et al. 2008 ). To identify subgroups (cluster) of similar papers within our dataset, we performed cluster analysis on our MCA results using HCPC. The cluster results are then visualised in factor map and dendrogram for easier interpretation using factoextra package. This allows us to identify categorical variables which have the highest effect within each cluster. Statistical analyses were considered significant at α = 0.05, while a p between 0.10 and 0.05 was considered as a tendency. The p-value is less than 5% when one category is significantly linked to other categories. The V tests show whether the category is over-expressed (positive values) or under-expressed (negative values) in the cluster (Lebart et al. 1995 ).

Results from the literature review were also analysed with VOSviewer, freeware for constructing and visualising bibliometric networks ( http://www.vosviewer.com/ ). The program uses clustering techniques to analyse co-authors, co-occurrence of keywords, citations, or co-citations (van Eck and Waltman 2014 ). First, we analyse co-authorships of countries to provide a geographical representation of groups of authors in various countries over the past 30 years. Each circle represents an author’s country and the size represents the collaboration frequency with other countries. The lines between the nodes represent the collaboration networks between the countries while the thickness of the lines indicates the collaboration intensities between them. Lastly, to complement the MCA and HCPC, we used VOSviewer to analyse a clustering solution of categories at an aggregate level. Aggregate clustering is a meta-clustering method to improve the robustness of clustering and does not require a priori information about the number of clusters. In this case, instead of author’s keywords, we used the co-occurrence of categories associated to each selected paper as input to run the software.

We identified a total of 698 papers published between January 1991 and November 2019 reporting consequences of habitat fragmentations corresponding to our selection criteria (Fig.  1 ). The complete list of studies included (hereafter termed “selected papers”) is available in Online Appendix 2. The distribution of these selected papers between focal groups and among continents was non-homogeneous (Fig.  2 ). Selected papers reviewed were predominantly studies which were conducted in North America 310 (44%) and Europe 131 (19%), but also from Oceania 104 (15%), South America 85 (12%), Asia 37 (5%) and Africa 31 (5%). For co-authorships between countries based on VOSviewer, the minimum document number of a country was set as 5 and a total of 21 and 14 countries met the threshold for amphibians and reptiles respectively (Fig.  3 ). For amphibians, countries in the American continent such as United States of America or USA (178 articles), Brazil (38 articles) and Canada (35 articles) have the largest research weight (Fig.  3 a). Authors from the USA have the largest international cooperation network, followed by Brazil. Australia and other European countries such as Germany, France and England also have high collaboration relationships with other countries. In contrast, reptile studies were mainly concentrated around two countries: the USA (139 articles) and Australia (86 articles) (Fig.  3 b). No other country from the rest of the world has more than 20 articles. While both the USA and Australia have the largest collaboration networks, Canada, Spain and Mexico are also highly cooperative with authors from other countries.

Map of study locations for a amphibians and b reptiles with each circle representing the study location of papers included in the review. The colour scale of the continents ranging from 0 – 0.9 indicates the proportions of amphibian and reptile species represented in the reviewed papers when compared to known species in the world (obtained from AmphibiaWeb and ReptileDatabase): a Europe (0.73), Africa (0.23), North America (0.23), South America (0.18), Oceania (0.07) and Asia (0.06) and b Europe (0.27), Oceania (0.18), Africa (0.12), North America (0.11), South America (0.09) and Asia (0.02)

Co-authorship map of countries involved in habitat fragmentation research in a amphibians and b reptiles. The colours represent the continents countries belong to. Each circle represents an author’s country and the size represents the collaboration frequency with other countries. The lines between the nodes represent the collaboration networks between the countries while the thickness of the lines indicates the collaboration intensities between them. Category co-occurrence network maps for c amphibians and d reptiles. The colour represents the different cluster groups each category belongs to. Abbreviations for the categories in forms of habitat change: fragmentation (FGM), agriculture (AGR), Logging (LOG), Mining (MIN), Urbanisation (URB), road (RD), other habitat fragmentation (OHC); sampling methods: genetics (GEN), direct tracking method (DTM), aerial photographs (APT), GIS/ Satellite images (GIS), experimental (EXP), prediction/ simulation models (PSM) and response variables: species richness/ diversity (SPR), functional richness/ species guild (FCR), presence/ absence (PAS), population (POP), abundance (ABD), dispersal (DSP), breeding sites (BRD), fitness measure (FIT), interspecific interaction (INT),extinction/ colonisation rate (ECR), microhabitat preference (MHP), comparison between generalist and specialist (CGS), other response varialbes (ORV) (see also Online Appendix 1). Maps are created in VOSviewer

Overall, over half of all selected papers included only amphibians (376 papers; 54%), whilst 276 papers (39%) included only reptiles and 46 papers (7%) assessed both reptiles and amphibians. In relation to species richness, we identified 1490 amphibian species and 1199 reptile species across all papers; among which 141 taxa were not identified to species level but were still included in our analyses as taxonomic units analogous to species (Online Appendix 2). Among these species, more than half of the studied amphibians were found in South America (537; 38%) and North America (328; 23%), followed by Africa (297; 21%), Asia (137; 10%), Europe (77; 5%), and Oceania (51; 3%). Half of the reptile species studied were from North America (302; 25%) and Africa (278; 23%), with the other half consisting of species from Oceania (276; 23%), South America (200; 17%), Europe (76; 6%), and Asia (67; 6%).

When compared to the known species richness in the world, large portions of European species are studied while species from other continents were severely under-represented (Fig.  2 ). The proportions of amphibian species represented in papers were the highest in Europe (73%), while the proportions are much lower for Africa (23%), North America (23%), South America (18%), Oceania (7%) and Asia (6%) (Fig.  2 a). Among reptiles, Europe represents again the highest proportion of studied species (27%), followed by Oceania (18%), Africa (12%), North America (11%) and South America (8.9%) (Fig.  2 b). In contrast, of all Asian reptile species, only a mere 1.73% were included in the selected papers. The species coverage in our selected papers does not seem optimistic. Amphibians and reptiles each have only six families with more than half of the species covered (including three reptilian families containing one species in total). Meanwhile, 23 and 25 families remain fully neglected for amphibians and reptiles respectively (Figs.  4 – 5 ).

Species coverage for each taxonomic family in selected papers of amphibians. The numbers on each row indicate the total number of species known in its respective family (obtained from AmphibiaWeb 2021 )

Species coverage for each taxonomic family in selected papers of reptiles. The numbers on each row indicate the total number of species known in its respective family (obtained from ReptileDatabase)

Multiple correspondence analysis provided important insights into underlying patterns in our data allowing us to visualise the relationship between forms of habitat fragmentation (Median = 1 [1–4]), sampling methods (Median = 1 [0–5]) and response variables (Median = 2 [1–6]). Percentage of variance (or eigenvalues) from MCA output represents the contribution of each dimension in explaining the observed patterns. The top ten new dimensions identified by MCA explained a total of 61.64% and 61.16% of the total variance for amphibians and reptiles respectively. The two dimensions with the highest variance percentages explained were found in the first (Dim1, 12.55%) and second (Dim2, 9.13%) dimensions in amphibians (Online Appendix 3–4). Genetics (sampling method; 13.73%) and population (response variable; 12.39%) contributed the most to Dim1, together with species richness (response variable;10.41%) and dispersal (response variable; 9.20%). For Dim2, experimental (sampling method; 14.38%) was the dominant variable, the rest was determined by GIS/Satellite images (sampling method; 9.71%), fitness measure (response variable; 9.12%) and urbanisation (form of fragmentation; 8.94%). For reptiles, the two dimensions explaining the most variation were the first (Dim1, 11.34%) and second (Dim2, 8.28%) dimensions (Online Appendix 3–4). The variables contributing the most to Dim1 were species richness (response variable; 15.51%), abundance (response variable; 10.11%), presence/absence (response variable; 6.97%) and genetics (sampling method; 6.39%). On the other hand, Dim2 was determined by interspecific interaction (response variable; 13.49%), genetics (12.79%), experimental methods (sampling method; 11.21%) and fitness measure (response variable; 10.94%). The contribution of each category to the definition of the dimensions is reported in Online Appendix 3. The categories identified in the MCA dimensions are subsequently used for building the distance matrix in the clustering analysis.

The HCPC analysis identified three clusters of variables for amphibians and reptiles (Online Appendix 5–6). The output of the HCPC analysis is reported in Online Appendix 7. V test represent the influence of variables in the cluster composition. In general, three clusters for both amphibians and reptiles appeared to be uniquely similar by definition of categories (Fig.  6 ). For amphibians, cluster 1 was defined by studies on species richness (p < 0.05, V test = 14.30) and presence/absence (p < 0.05, V test = 13.42), while cluster 2 was determined by experimental studies (p < 0.05, V test = 10.95) and fitness measures (p < 0.05, V test = 9.77). Cluster 3 was defined by genetics (p < 0.05, V test = 18.44) and population studies (p < 0.05, V test = 17.73) (Online Appendix 7). Abundance and functional richness were also unique to cluster 1; other response variables and direct tracking methods were important to cluster 2 and dispersal was present in cluster 3 even though these variables are less expressed (Fig.  6 a).

Percentage contribution of the categories contributing to the uniqueness of each cluster in amphibians (Dark green = 1, Bright green = 2, Bright yellow = 3) and reptiles (Dark red = 1, Orange = 2, Dark yellow = 3) based on the Cla/Mod results of HCPC (see Online Appendix 7). Abbreviations for the categories can be found in Fig.  3 and in Online Appendix 1

For reptiles, cluster 1 was represented by species richness (p < 0.05, V test = 14.26), abundance (p < 0.05, V test = 11.22) and presence absence (p < 0.05, V test = 8.55) papers, whereas cluster 2 was determined by papers on fitness measures (p < 0.05, V test = 10.99), direct tracking methods (p < 0.05, V test = 8.64) and interspecific interaction (p < 0.05, V test = 7.86), and cluster 3 was defined by genetics (p < 0.05, V test = 12.79), population (p < 0.05, V test = 9.95) and prediction/simulation models (p < 0.05, V test = 7.68) papers (Online Appendix 7). Although slightly less expressed in the clusters, papers using comparisons between generalist and specialist species and papers on functional richness were also unique to cluster 1; experimental methods and other response variables were heavily present in cluster 2, while dispersal studies were distinct to cluster 3 (Fig.  6 b).

Results from VOSviewer categories of both amphibians and reptiles appear to be similar to each other (Fig.  3 c, d). The clustering of the categories in the co-occurrence network maps confirms what we observed in the HCPC results (Fig.  6 ). In addition to geographical representation of study locations in (1), the corresponding clusters of selected papers are also mapped in Figs.  7 and 8 to investigate the spatial grouping patterns for the three clusters (see Online Appendix 8–9 for geographical representation for each category). We also plotted the temporal trend in Online Appendix 10 and 11. Overall, the three clusters are distributed homogeneously across the globe, but concentrated in the USA, Europe and south eastern Australia. Cluster 1 papers were found to be the most predominant cluster in amphibians (57% papers) across all continents (see Online Appendix 12; Fig.  7 ). When compared to other clusters, studies from this cluster are often conducted in Afrotropics, particularly Madagascar (100% papers), central (Costa Rica (60% papers) and Mexico (92% papers) and south America (80% papers) (Online Appendix 12, Figs.  7 , 8 ). On the other hand, cluster 2 papers appear to be more prevalent for reptile studies compared to amphibian studies, with a higher number of studies conducted across North America (65 to 51) and Australia (22 to 2) (Figs.  7 , 8 ). Lastly, a vast majority of cluster 3 papers were located in North America and Europe (both contributing to 79% of the papers) for amphibians and North America and Australia (both contributing to 84% of the papers) for reptiles (Online Appendix 12, Figs.  7 , 8 ). Publications from this cluster started to gain popularity from 2005 onwards, following similar increasing trends as cluster 2 (Online Appendix 10–11). Overall, except for cluster 1 in South America, most of the clusters in Asia and Africa appear to experience very little or no increase in publications over the years (Online Appendix 10–11).

Map of the individual selected papers belonging to each cluster groups (Dark green = 1, Bright green = 2, Bright yellow = 3) for amphibians, with each circle representing the study location. The colour scale of the continents ranging from 0 to 0.9 indicates the proportions of amphibian species represented in the reviewed papers when compared to known species in the world (obtained from AmphibiaWeb)

Map of the individual selected papers belonging to each cluster groups (Dark red=1, Orange=2, Dark yellow=3) for reptiles, with each circle representing the study location. The colour scale of the continents ranging from 0.0 – 0.9 indicates the proportions of reptile species represented in the reviewed papers when compared to known species in the world (obtained from ReptileDatabase).

Our review found no improvement in the geographical and taxonomic bias in habitat fragmentation studies for both reptiles and amphibians compared to earlier studies (Fardila et al. 2017 ). Yet, our study has made an effective contribution towards identifying major spatial gaps in habitat fragmentation studies over the past three decades (updating reviews such as Cushman 2006 ; Gardner et al. 2007 )). In particular, we found an overall increase in the number of studies measuring species richness and abundance throughout the years while population-level and genetics studies are still lacking in developing countries. Here, we discuss the issues of (1) biogeographical bias, (2) the extent and focus of habitat fragmentation research and (3) the limitations and knowledge gaps in habitat fragmentation research in herpetology and provide recommendations for future research.

Biogeographical bias

Geographic bias in research papers.

Given the research effort in relatively wealthy countries (Holmgren and Schnitzer 2004 ; Fazey et al. 2005 ) it is not surprising that more than half the papers concern North America and Europe, where there is strong prevalence of herpetological research. This pattern is also evident in other taxonomic groups and biological areas including invasion biology (Pyšek et al. 2008 ), biodiversity conservation (Trimble and Aarde 2012 ; Christie et al. 2020 ), and habitat fragmentation (Fardila et al. 2017 ). The USA alone contributed more than a third of the publications in terms of both authors and location of study (Fazey et al. 2005 ; Melles et al. 2019 ). English speaking countries including the USA, the United Kingdom, and Australia have dominated research output over the last 30 years (Melles et al. 2019 ). These patterns were reflected in the collaboration network maps generated by VOSviewer (Fig.  3 ). Similar hotspots found between who does the research (Fig.  3 ) and the study locations (Fig.  2 ) suggest that authors tend not to move much and only to study ecosystems near to where they are based (Meyer et al. 2015 ). One reason for this bias is the distance to field sites accentuated by the costs and time of travelling.

However, the near absence of studies from many parts of the world that are currently under extreme pressures of habitat loss and degradation are of great concern (Habel et al. 2019 ). We feel that the level of threat associated with habitat fragmentation in these continents is not proportional to the level of research attention required. Naturally biodiverse but less economically developed Southeast Asian and sub-Saharan countries will suffer greatest diversity losses in the next century (Newbold et al. 2015 ). If this persists at the current rate, biodiverse areas will likely disappear before new discoveries in those hotspots are made (Moura and Jetz 2021 ). Although conversely our study found that among other developing countries Brazil is currently conducting relatively more in-country amphibian studies and collaboration with other countries. However, how much of this information reaches decision makers and practitioners remains unknown. This is largely due to the lack of intermediary evidence bridges (Kadykalo et al. 2021 ). These intermediaries identify evidence summaries based on research and priorities and distribute them to practitioners, facilitating exchange of knowledge between and among researchers and practitioners (Holderegger et al. 2019 ; Kadykalo et al. 2021 ).

Geographic bias in focal groups

Congruent to results reported in Gardner et al. ( 2007 ), studies on amphibians were more abundant than studies on reptiles. Over the past years, there has been a strong focus on amphibian population declines. This was catalysed by the emergence of chytridiomycosis and global decline of amphibians (Fisher and Garner 2020 ). Amphibians, and predominantly frogs, are the principal focus of herpetological research, with the highest allocation of resources and the highest publication rates (Ferronato 2019 ). Another reason for this bias may be that amphibians serve as valuable indicators of environmental stress and degradation owing to their aquatic and terrestrial lifestyle and permeable skin (Green 2003 ). These attributes make them extremely sensitive to changes in temperature and precipitation as well as pollution (Sodhi et al. 2008 ). Lizards, also susceptible to temperature changes, however, are characterised by a high degree of endemism, restricted geographic ranges, late maturity, a long life-span and are thus very susceptible to population declines (Todd et al. 2010 ; Meiri et al. 2018 ). Certain groups of reptiles, such as worm lizards and blind snakes lead cryptic and solitary lives in contrast to the large breeding aggregations and choruses of, for example, frogs. Such characteristics make them difficult for researchers to study as they require large amount of search effort for little data (Thompson 2013 ).

• Taxonomic bias

We found a heightened geographical bias in the taxonomic coverage of studies. Given the sheer number of selected papers investigated, it is not surprising that the continents of North and South America cover more than half of the amphibian species studied whereas North America and Africa cover almost half of the reptile species studied. This trend broadly mirrors the geographic distribution pattern of the global described species in both these taxa (AmphibiaWeb 2021 ; Uetz et al. 2021 ). While a large proportion of the known European and North American families such as Alytidae and Ambystomatidae have been investigated (Fig.  4 ), species from other continents remain severely under-represented. Yet, the European continent represents only 2% of the described species globally. This high research intensity bias in low biodiverse regions of the world has been noted previously (Fazey et al. 2005 ). In general, reptiles and amphibians have been disproportionately poorly studied in the tropics and in developing areas despite that these areas show among the highest rates of deforestation and a corresponding rise in the number of threatened species (Böhm et al. 2013 ; Deikumah et al. 2014 ). These biodiverse areas largely consist of threatened species having restricted home ranges (Meiri et al. 2018 ). Even though we observed a great fraction of the species investigated in the Afrotropics (Vallan 2002 ; Hillers et al. 2008 ; Ofori‐Boateng et al. 2013 ; Riemann et al. 2015 ; Blumgart et al. 2017 ), especially Madagascar (see Mantellidae and Opluridae in Fig.  4 ), it seems insufficient when considering that an estimated 3.94 million hectares of forest area of the continent was cleared yearly over the last century (FAO and UNEP 2020 ). Further, biodiverse hotspots such as the neotropical regions and Indo-Malayan tropics have the highest chances of new species of amphibians and reptiles being discovered (Moura and Jetz 2021 ).

Being herpetofauna diversity hotspots, countries in South America and Asia are indeed understudied. Although Brazil has a high number of amphibian studies, less than one percent of known reptile species was studied in both continents (Fig.  2 ). A number of factors contribute to this lack of representation. First, there is an overwhelming number of new species being discovered every year in these hotspots (Moura et al. 2018 ; Moura and Jetz 2021 ). Furthermore, newly discovered species tend to belong to more secretive groups such as burrowing snakes, worm lizards and caecilians (Colli et al. 2016 ). Yet, these fossorial organisms are clearly neglected in fragmentation studies (see Fig.  4 – 5 ) with researchers focusing on well-known taxonomic groups (Böhm et al. 2013 ). On a positive note, despite having the country (Australia) with the highest reptile diversity (Uetz et al. 2021 ), Oceania represented a fair coverage of reptile diversity compared to other continents. Since 2001, there has been an increase of fragmentation studies in Australia (e.g., Brown 2001 ; Mac Nally and Brown 2001 ; Hazell et al. 2001 ) and there is a continuing increase in research output (Melles et al. 2019 ), contributing 85 out of 89 reviewed studies in Oceania over the last 30 years.

Extent and focus of research

Our findings showed important associations between methods and response metrics but not different forms of habitat fragmentation. This either suggests that researchers were not favouring any sampling method and response variable for evaluating the effects of certain habitat fragmentation or this pattern may occur due to a relatively even split of papers dealing with different forms or combinations of habitat fragmentation in the clusters. In general, species richness or diversity appears to explain most of the variation in our data ( see Online Appendix 4 ). While species richness remains a popular diversity metric employed in conservation biology (Online Appendix 12; also see Gardner et al. 2007 ), we also found an increasing trend in the use of genetic techniques for habitat fragmentation studies. More specifically in recent years, molecular genetics have become popular and are often studied together with population connectivity to capture species responses to habitat fragmentation ( see Online Appendix 4 ) (Keyghobadi 2007 ). The HCPC approach identified three main clusters of research fields which will be referred to as research agendas from here onwards. Contrary to our expectation, we did not find a global spatial pattern of research agendas, but instead a rather homogeneous distribution of papers, possibly due to the lack of selected studies which are found in developing countries outside USA, Europe and Australia (Figs.  7 , 8 ). This nevertheless indicates that different sampling methods are shared and used between leading herpetological experts from different countries and that there are continuing collaborations between countries, particularly in North America and Europe.

Below, we describe the research agendas and their corresponding categories (Fig.  6 ) that have contributed significantly to the study of habitat fragmentation for the past 30 years: (a) Agenda 1: Measures of direct individual species responses, (b) Agenda 2: Physiological and movement ecology, and (c) Agenda 3: Technology advancement in conservation research.

Agenda 1: Measures of direct individual species responses

We found that the majority of studies around the globe evaluated patterns of assemblage richness, species presence/absence, and abundance (Figs.  7 , 8 ). These simple patterns of richness, diversity and abundance are the most common responses measured because they provide a good indication of species response to habitat fragmentation and are easy to calculate (Colwell 2009 ). Although species richness does not consider abundance or biomass but treats each species as of equal importance to diversity, species evenness weighs each species by its relative abundance (Hill 1972). Further, composite measures like species diversity indices (e.g., Simpson’s 1/D or Shannon’s H) combine both richness and evenness in a single measure (Colwell 2009 ), preventing biases in results. However, directly measuring these species responses might not be ecologically relevant as they fail to account for patterns in species assemblage turnover. In fact, few selected papers (38 out of 697) in our study have attempted to categorise species into meaningful functional groups or guilds, despite that the categorisation of ecological functions such as habitat preference, taxonomic family, reproductive mode, and body size can be easily done (but see Knutson et al. 1999 ; Peltzer et al. 2006 ; Moreira and Maltchik 2014 ). Knutson et al.( 1999 ) was the first in our selected papers to group species with similar life-history characteristics into guilds and to examine their responses to landscape features. They observed negative associations between urban land use and anuran guilds. Analyses of guilds or functional groups can reveal contradictory results (but not always, see Moreira and Maltchik 2014 ). For example, the species richness of anurans in logged areas of West Africa is found to be as high as in primary habitat (Ernst et al. 2006 ). Yet, analyses of functional groups indicated significantly higher diversity in primary forest communities (Ernst et al. 2006 ). Similar differences were also observed for species with varying degrees of niche overlaps, habitat specialists, and for different continents (Ernst et al. 2006 ; Seshadri 2014 ). These results underline that species richness alone is a poor indicator of the functional value of species in the ecosystem as the relationships between functional diversity and species richness are inconsistent and can sometimes be redundant (functional diversity remains constant if assemblages are functionally similar; Riemann et al. 2017 ; Palmeirim et al. 2017 ; Silva et al. 2022 ). The results of some species richness studies may consequently provide misleading inferences regarding consequences of habitat fragmentation and conservation management (Gardner et al. 2007 ).

Although not substantially greater than the agendas 2 and 3, the measure of individual species responses has always been popular across the globe but also increasingly popular in the tropical and subtropical regions (e.g., South America and Africa; Online Appendix 10–11). For example, a research team led by Mark-Oliver Roedel from Germany has conducted numerous studies on Afrotropical amphibian communities (Hillers et al. 2008 ; Ofori‐Boateng et al. 2013 ; Riemann et al. 2017 ). Due to the higher biodiversity and species rarity in these regions compared to temperate areas, it is reasonable to expect a greater level of sampling effort in patterns of species richness, abundance, and guild assemblage to obtain comparisons of diversity with sufficient statistical power across different land use changes (Gardner et al. 2007 ). Access to highly specific expertise and most up to date methods and technology may not be available in these regions, and as such, study designs are limited to multispecies survey addressing simple patterns of diversity and species assemblages (Hetu et al. 2019 ). Unfortunately at the same time, these forest biomes holding the highest richness and abundance of amphibians and reptiles have showed consistent negative responses to land use changes (Cordier et al. 2021 ).

Agenda 2: physiological and movement ecology

We did not observe a strong association between occupancy and dispersal in our study. Perhaps this is because only a few papers investigated dispersal via habitat occupancy compared to the overwhelming proportions of papers examining the presence of species in response to habitat fragmentation in research agenda 1. Similarly, few studies measure dispersal with direct tracking methods, with the majority that discussed dispersal being based on indirect inferences, such as genetic divergence (see Fig.  3 c, d; Driscoll et al. 2014 ). Genetic approaches can be effective in situations where more direct approaches are not possible (Lowe and Allendorf 2010 ). For instance, using microsatellites and mitochondrial DNA, Buckland et al. ( 2014 ) found no migration occurring between isolated subpopulations of a forest day gecko ( Phelsuma guimbeaui ) in a fragmented forest and predicted a dramatic decrease in survival and allelic diversity in the next 50 years if no migration occurs (Buckland et al. 2014 ). In some cases, molecular markers also allow direct dispersal studies by assigning individuals to their parents or population of origin (Manel et al. 2005 ). However, there are limitations on when these techniques can be applied. Assignment tests require appropriate choices of molecular markers and sampling design to permit quantification of indices of dispersal (Broquet and Petit 2009 ; Lowe and Allendorf 2010 ). Parent–offspring analysis is constrained by the uncertainty in assessing whether offspring dispersal is completed at the time of sampling and sample size (Broquet and Petit 2009 ). Genetic tools may thus be best applied in combination with direct approaches because they contain complementary information (Lowe and Allendorf 2010 ; Safner et al. 2011 ; Smith et al. 2016 ).

Traditional approaches in habitat fragmentation research like radiotracking or capture-mark-recapture of animals can be effective in evaluating dispersal and ecological connectivity between populations. For example, based on mark-recapture data over a nine year period, facultative dispersal rates in an endangered amphibian ( Bombina variegata ) were found to be sex biased and relatively low from resulting patch loss (Cayuela et al. 2018 ). In our case, direct tracking methods are more commonly and effectively used in examining the impacts of habitat modification on changes in ecology directly relating to fitness (Fig.  6 ): home ranges (Price-Rees et al. 2013 ), foraging grounds (MacDonald et al. 2012 ) and survival rates (Breininger et al. 2012 ). Yet, such routine movements associated with resource exploitation do not reflect the biological reality and evolutionary consequences of how organisms change as landscape changes (Van Dyck and Baguette 2005 ). Instead, directed behavioural movements affecting dispersal processes (emigration, displacement or immigration) are crucial in determining the functional connectivity between populations in a fragmented landscape (Bonte et al. 2012 ). In one study, spotted salamanders Ambystoma maculatum tracked with fluorescent powder exhibited strong edge mediated behaviour when dispersing across borders between forest and field habitats and can perceive forest habitats from some distance (Pittman and Semlitsch 2013 ). Knowing such behaviour rules can improve predictions of the effects of habitat configuration on survival and dispersal. However, ongoing conversion of natural ecosystems to human modified land cover increases the need to consider various cover types that may be permeable to animal movements. As such, experimental approaches can be effective in examining the effect of matrix type on species movements as seen in our results (Fig.  6 ) (Rothermel and Semlitsch 2002 ; Mazerolle and Desrochers 2005 ). For example, researchers conducted experimental releases of post-metamorphic individuals of forest amphibians into different substrates and mapped the movements of paths and performance (Cline and Hunter Jr 2016 ). They showed that non-forest matrices with lower structural complexity influence the ability of frogs to travel across open cover and to orient themselves towards the forest from distances greater than 40–55 m. Therefore, it is inaccurate to assume matrix permeability to be uniform across all open-matrix types, particularly in amphibians (Cline and Hunter 2014 , 2016 ).

In addition, the ability to move and disperse is highly dependent on the range of external environments and internal physiological limits (Bonte et al. 2012 ), especially in reptiles and amphibians (Nowakowski et al. 2017 ). The study of physiological effects on movement was seen throughout our selected studies (Fig.  6 ). For example, higher temperatures and lower soil moisture in open habitats could increase evaporative water loss in salamanders (Rothermel and Semlitsch 2002 ). Other tests including interaction effects between landscape configuration and physiological constraints (e.g., dehydration rate Rothermel and Semlitsch 2002 ; Watling and Braga 2015 ); body size (Doherty et al. 2019 ) can be useful to better understand fitness and population persistence. We argue here that multidisciplinary projects examining movement physiology, behaviour and environmental constraints in addition to measuring distance moved are needed to progress this field.

Our results indicate a high bias of agenda 2 papers represented among developed countries, with a strong focus on reptiles compared to amphibians (Price-Rees et al. 2013 ; Doherty et al. 2019 ) (Online Appendix 12, Figs.  7 , 8 ). The adoption of direct tracking as well as genetic methods can be cost prohibitive in developing and poorer regions. However, cheaper and simpler methods to track individuals are increasing (Mennill et al. 2012 ; Cline and Hunter 2014 , 2016 ). Although existing application might not be ideal for reptiles and amphibians, new technologies for tagging and tracking small vertebrates are being developed including acoustic surveys and improved genetic methods (Broquet and Petit 2009 ; Mennill et al. 2012 ; Marques et al. 2013 ). While there are many improvements needed to obtain better quality dispersal data studies on movement ecology, reptiles and amphibians still only account for a mere 2.2% of the studies on dispersal when compared to plants and invertebrates which comprised over half of the studies based on a systematic review (Driscoll et al. 2014 ). Thus, we urge more studies to be conducted on these lesser-known taxa, especially in biodiverse regions. Given the limited dispersal in amphibians and reptiles, having a deeper understanding on their dispersal can be critical for the effective management and conservation of populations and metapopulations (Smith and Green 2005 ).

Agenda 3: technology advancement in conservation research

While community level approaches such as responses in species richness, occupancy, and abundance measure biodiversity response to habitat fragmentation, they are limited in inference because they do not reflect patterns of fitness across environmental gradients and landscape patterns. Instead, genetic structure at the population level can offer a higher resolution of species responses (Manel and Holderegger 2013 ). For instance, genetic erosion heavily affects the rate of species loss in many amphibian species (Allentoft and O’Brien 2010 ; Rivera‐Ortíz et al. 2015 ). Over the past decades we have seen a rapid increase in studies applying genetic analysis to assess the effects of habitat fragmentation (Keyghobadi 2007 ), reflecting the strength of these approaches. This growth is mostly evident in North America and Europe (but also Oceania for reptiles) ( Online Appendix 10–11). The availability of different genetic markers has been increasing, from microsatellites in the 1990s then shifting towards genotyping by sequencing (NGS) technologies that enable rapid genome-wide development (Allendorf et al. 2010 ; Monteiro et al. 2019 ). However, the study of population structure alone can lead to misleading results as environmental changes to species dynamics are not considered. The resistance imposed by landscape features on the dispersal of animals can ultimately shape gene flow and genetic structure (Bani et al. 2015 ; Pilliod et al. 2015 ; Monteiro et al. 2019 ).

To understand this, researchers combine genetic, land cover and climate variables to study the gene flow patterns across heterogeneous and fragmented landscapes (Manel and Holderegger 2013 ). Spatial analyses can be a powerful tool for monitoring biodiversity by quantifying environmental and landscape parameters. The growing interest in both landcover data and the rapid development of computer processing power prompted the development of new prediction methods, primarily in spatial models (Ray et al. 2002 ), ecological niche modelling (Urbina-Cardona and Loyola 2008 ; Tan et al. 2021 ), and landscape connectivity (Cushman et al. 2013 ; Ashrafzadeh et al. 2019 ). In some cases, niche models are useful in assessing the effectiveness of protected areas for endangered species (Urbina-Cardona and Loyola 2008 ; Tan et al. 2021 ).

The integration of genetic data in ecological niche models for recognising possible dispersal movements between populations were observed in our study (Fig.  3 c, d), especially in reptiles (Fig.  6b ). The hallmark of landscape genetics is the ability to estimate functional connectivity among populations and offer empirical approach of adaptive genetic variation in real landscapes to detect environmental factors driving evolutionary adaptation. The most common approach of landscape genetics is determining whether effective distances as determined by the presence of suitable habitat between populations, better predict genetic distances than do Euclidean distances (assuming spatially homogeneous landscape). However, straight-line geographic distance does not normally reflect true patterns of dispersal as landscape barriers or facilitators in a heterogeneous landscape could strongly affect gene flow (Emel and Storfer 2012 ; Fenderson et al. 2020 ). Therefore, in these cases, ecological distances or landscape resistance can often explain a greater deal of genetic variation between fragmented populations (Cushman 2006 ; Bani et al. 2015 ). Using a combination of habitat suitability modelling (e.g., Maxent, Phillips et al. 2017 ), multiple least-cost paths (LCPs) (Adriaensen et al. 2003 ) and the more recent circuit theory analysis (McRae et al. 2008 ) to investigate landscape resistance can be highly effective predicting potential pathways along which dispersal may occur, hence informing conservation management (Emel and Storfer 2012 ; Bani et al. 2015 ; Pilliod et al. 2015 ). To date, landscape genetics has been shown to be particularly useful in studying organisms with complex life histories (Emel and Storfer 2012 ; Shaffer et al. 2015 ). Yet, the applications of landscape genetics have been limited to contemporary patterns using modern genetic data. Few studies have benefitted from the inclusion of temporal genetic data (Fenderson et al. 2020 ). For example, historical DNA samples and heterochronous analyses could allow us to explore how anthropogenic impacts have affected past genetic diversity and population dynamics (Pacioni et al. 2015 ) and identify areas of future suitability of endangered animals in face of climate change (Nogués-Bravo et al. 2016 ). The possibility to investigate migration through spatiotemporal population connectivity can greatly improve the prediction of species responses under future landscape and climate change scenarios (Fenderson et al. 2020 ).

Population genetic and niche modelling studies for both taxa are rarely found in developing regions of the world, especially in Asia and Africa (Figs.  7 , 8 ). Even though conservation priorities are concentrated in these biodiverse regions, invaluable highly specific expertise such as conservation genetics and other contemporary methodologies might not be readily available due to lack of funding and infrastructure (Hetu et al. 2019 ). Thus, we encourage collaborations with the poorer countries initiated by foreign service providers from developed countries. Contrary to expectations, very few studies on conservation genetics were found in China and Japan despite their vast advances in genetic techniques. Fortunately, China has made substantial progress in the last 20 years in understanding human genetic history and interpreting genetic studies of human diseases (Forero et al. 2016 ) as well as biodiversity conservation (Wang et al. 2020 ), yet the same cannot be said for conservation genetics on reptiles and amphibians (Figs.  7 , 8 ), but see Fan et al. ( 2018 ) and Hu et al. ( 2021 ).

Limitations and knowledge gaps

The forms of habitat fragmentation which we categorised may not reflect the ecological impact in the real world as interactions between different habitat fragmentation forms were not accounted for. Although each of these forms of habitat fragmentation possesses serious environmental consequences, their combination could have severe synergistic impacts (Blaustein and Kiesecker 2002 ). For example, a fragmented landscape is not just reduced and isolated, but subject to other anthropogenic disturbances such as hunting, fire, invasive species, and pollution (Laurance and Useche 2009 ; Lazzari et al. 2022 ). Altered climatic conditions and emerging pathogens such as batrachochytrids can also interact with each other, and other threats (Fisher and Garner 2020 ). The use of habitat suitability models based on climatic scenarios, combined with hydrological and urbanisation models, are effective in detecting best to worst case scenarios and local extinctions, as shown for the spotted marsh frog ( Limnodynastes tasmaniensis ) (Wilson et al. 2013 ).

We acknowledge the bias of scientific research introduced from the limitation of search term to English-speaking literature on the geographic distribution of the papers we sampled (Konno et al. 2020 ; Angulo et al. 2021 ). In Latin American journals for example, we found a number of papers published in Spanish, but unfortunately, they did not fit the criteria of our selection (see Online Appendix 2). Conservation studies written in languages other than English are often published in local journals which do not normally go through international peer review.

The homogeneous distribution of the research agendas across geographical regions in our study may be explained by the lack of studies found in South America, Asia and Africa, preventing us to see a potentially dichotomous spatial pattern among the clusters. However, this reflects the current state of research and the challenges faced in less developed countries.

(4) Our study did not investigate whether habitat fragmentation has led to an improved or decreased biotic response. Predicting species response to habitat modification has been reviewed countless times (Rytwinski and Fahrig 2012 ; Driscoll et al. 2014 ; Doherty et al. 2020 ; Newbold et al. 2020 ; Cordier et al. 2021 ). Yet, these reviews often yield little or no general patterns (Doherty et al. 2020 ; Cordier et al. 2021 ). Response variables or traits measured are often found to be poor predictors of the impacts of habitat fragmentation. There are two possible explanations for this discrepancy. First, the strength and direction of the responses differs between species, ecophysiological groups (Rothermel and Semlitsch 2002 ), and phylogenetic or functional groups (Mazerolle and Desrochers 2005 ; Nowakowski et al. 2017 ). Second, responses in animals to different types of disturbance may be specific to the ecosystem where they live. Different biogeographic regions or biomes have different characteristics affecting local species (Lindell et al. 2007 ; Blowes et al. 2019 ; Newbold et al. 2020 ; Cordier et al. 2021 ).

Conclusions and recommendations

Our results underline promising research fields and geographic areas and may serve as a guideline or starting point for future habitat fragmentation studies. We suspect similar paradigms of geographic and thematic patterns to occur in other taxonomic groups.

Although studies dealing with habitat fragmentation impacts on mammals and birds are already widely recognised (Fardila et al. 2017 ), research on reptiles and amphibians has been lacking. We argue that amphibians and reptiles need more attention as they are equally or more threatened but highly neglected (Rytwinski and Fahrig 2012 ; Ferronato 2019 ; Cox et al. 2022 ).

Greater investment is required for studies in tropical and subtropical areas (Segovia et al. 2020 ), especially within the Asian continent. These areas are currently experiencing the highest rates of habitat loss (McDonald et al. 2013 ). Tropical specialists are further restricted to smaller geographic range sizes according to Rapoport’s rule which states that there is a positive latitudinal correlation with range size (Stevens 1989 ) (at least for amphibians in the Northern hemisphere where there is higher temperature and precipitation seasonality; Whitton et al. 2012 ). Having a small range size is often associated with negative responses to habitat modification (Doherty et al. 2020 ). Thus, more effort is needed in developing countries where the crisis is greatest and there is lack of funding and strong language barriers (Fazey et al. 2005 ). There is an urgent need to better integrate studies published in languages other than English with the broader international literature. Useful integration actions include training of local conservation biologists and promoting partnerships and research visits in these regions may have greater conservation consequences to understand global patterns of habitat modification (Meyer et al. 2015 ). Doing so will help remediate the sampling bias towards temperate generalists and will shed light on the fate of tropical specialists.

We encourage improved access to intermediary evidence-based conservation data (Kadykalo et al. 2021 ). Even when well-established genetic and genomic analyses have been proven to be promising area in herpetological conservation (Shaffer et al. 2015 ), there is a general lack of the transfer of knowledge between scientists and practitioners (Holderegger et al. 2019 ). As practitioners are generally interested in species monitoring and the evaluation of success of connectivity measures, an establishment of scientist-practitioner community to facilitate a platform for international exchange would help tremendously in future conservation planning and management (Holderegger et al. 2019 ).

Although different study designs and landscape measures have different strengths and limitations depending on the study objectives, we suggest reporting basic data to describe the effect of habitat fragmentation using standardised sampling methods, indices, and design (Holderegger et al. 2019 ). The results will allow future meta-analyses to be performed.

Incorporate remote sensing data, whenever possible, in studies involving habitat change and fragmentation. The use of niche modelling techniques combined with high resolution remote sensing has been instrumental in detecting potentially fragmented populations. With advances in landscape genomics, we are now able to examine the correlation between environmental factors and genomic data in natural populations (Manel and Holderegger 2013 ; Shaffer et al. 2015 ). Adopting such tools would be valuable in understanding how habitat amounts and configurations affect dispersal, survival, and population dynamics as well as the impacts of anthropogenic changes such as climate change (Shaffer et al. 2015 ).

Data availability

The datasets generated during the current study are available in Online Appendix 1. Codes used in the analyses are available from corresponding author on request.

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Acknowledgements

W.C. Tan was supported financially through a scholarship by the German Academic Exchange Service (DAAD). This work would not be possible without M. Flecks for his invaluable technical assistance with the figures.

Open Access funding enabled and organized by Projekt DEAL.

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Herpetology Section, LIB, Museum Koenig, Bonn, Leibniz Institute for the Analysis of Biodiversity Change, Adenauerallee 127, 53113, Bonn, Germany

W. C. Tan & D. Rödder

UMR 7179 C.N.R.S/M.N.H.N., Département Adaptations du Vivant, Bâtiment d’Anatomie Comparée, 55 Rue Buffon, 75005, Paris, France

Department of Biology, Evolutionary Morphology of Vertebrates, Ghent University, K.L. Ledeganckstraat 35, 9000, Gent, Belgium

Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610, Antwerpen, Belgium

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WCT, AH, and DR contributed to the study idea and conception. Literature search and data collection were performed by WCT and data analysis by DR and WCT. The first draft of the manuscript was written by WCT and all authors critically revised on later versions. All authors read and approved the final manuscript.

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Tan, W.C., Herrel, A. & Rödder, D. A global analysis of habitat fragmentation research in reptiles and amphibians: what have we done so far?. Biodivers Conserv 32 , 439–468 (2023). https://doi.org/10.1007/s10531-022-02530-6

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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• The four main types of essay | Quick guide with examples

The Four Main Types of Essay | Quick Guide with Examples

Published on September 4, 2020 by Jack Caulfield . Revised on July 23, 2023.

An essay is a focused piece of writing designed to inform or persuade. There are many different types of essay, but they are often defined in four categories: argumentative, expository, narrative, and descriptive essays.

Argumentative and expository essays are focused on conveying information and making clear points, while narrative and descriptive essays are about exercising creativity and writing in an interesting way. At university level, argumentative essays are the most common type. 

In high school and college, you will also often have to write textual analysis essays, which test your skills in close reading and interpretation.

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Table of contents

Argumentative essays, expository essays, narrative essays, descriptive essays, textual analysis essays, other interesting articles, frequently asked questions about types of essays.

An argumentative essay presents an extended, evidence-based argument. It requires a strong thesis statement —a clearly defined stance on your topic. Your aim is to convince the reader of your thesis using evidence (such as quotations ) and analysis.

Argumentative essays test your ability to research and present your own position on a topic. This is the most common type of essay at college level—most papers you write will involve some kind of argumentation.

The essay is divided into an introduction, body, and conclusion:

• The introduction provides your topic and thesis statement
• The body presents your evidence and arguments
• The conclusion summarizes your argument and emphasizes its importance

The example below is a paragraph from the body of an argumentative essay about the effects of the internet on education. Mouse over it to learn more.

A common frustration for teachers is students’ use of Wikipedia as a source in their writing. Its prevalence among students is not exaggerated; a survey found that the vast majority of the students surveyed used Wikipedia (Head & Eisenberg, 2010). An article in The Guardian stresses a common objection to its use: “a reliance on Wikipedia can discourage students from engaging with genuine academic writing” (Coomer, 2013). Teachers are clearly not mistaken in viewing Wikipedia usage as ubiquitous among their students; but the claim that it discourages engagement with academic sources requires further investigation. This point is treated as self-evident by many teachers, but Wikipedia itself explicitly encourages students to look into other sources. Its articles often provide references to academic publications and include warning notes where citations are missing; the site’s own guidelines for research make clear that it should be used as a starting point, emphasizing that users should always “read the references and check whether they really do support what the article says” (“Wikipedia:Researching with Wikipedia,” 2020). Indeed, for many students, Wikipedia is their first encounter with the concepts of citation and referencing. The use of Wikipedia therefore has a positive side that merits deeper consideration than it often receives.

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An expository essay provides a clear, focused explanation of a topic. It doesn’t require an original argument, just a balanced and well-organized view of the topic.

Expository essays test your familiarity with a topic and your ability to organize and convey information. They are commonly assigned at high school or in exam questions at college level.

The introduction of an expository essay states your topic and provides some general background, the body presents the details, and the conclusion summarizes the information presented.

A typical body paragraph from an expository essay about the invention of the printing press is shown below. Mouse over it to learn more.

The invention of the printing press in 1440 changed this situation dramatically. Johannes Gutenberg, who had worked as a goldsmith, used his knowledge of metals in the design of the press. He made his type from an alloy of lead, tin, and antimony, whose durability allowed for the reliable production of high-quality books. This new technology allowed texts to be reproduced and disseminated on a much larger scale than was previously possible. The Gutenberg Bible appeared in the 1450s, and a large number of printing presses sprang up across the continent in the following decades. Gutenberg’s invention rapidly transformed cultural production in Europe; among other things, it would lead to the Protestant Reformation.

A narrative essay is one that tells a story. This is usually a story about a personal experience you had, but it may also be an imaginative exploration of something you have not experienced.

Narrative essays test your ability to build up a narrative in an engaging, well-structured way. They are much more personal and creative than other kinds of academic writing . Writing a personal statement for an application requires the same skills as a narrative essay.

A narrative essay isn’t strictly divided into introduction, body, and conclusion, but it should still begin by setting up the narrative and finish by expressing the point of the story—what you learned from your experience, or why it made an impression on you.

Mouse over the example below, a short narrative essay responding to the prompt “Write about an experience where you learned something about yourself,” to explore its structure.

Since elementary school, I have always favored subjects like science and math over the humanities. My instinct was always to think of these subjects as more solid and serious than classes like English. If there was no right answer, I thought, why bother? But recently I had an experience that taught me my academic interests are more flexible than I had thought: I took my first philosophy class.

Before I entered the classroom, I was skeptical. I waited outside with the other students and wondered what exactly philosophy would involve—I really had no idea. I imagined something pretty abstract: long, stilted conversations pondering the meaning of life. But what I got was something quite different.

A young man in jeans, Mr. Jones—“but you can call me Rob”—was far from the white-haired, buttoned-up old man I had half-expected. And rather than pulling us into pedantic arguments about obscure philosophical points, Rob engaged us on our level. To talk free will, we looked at our own choices. To talk ethics, we looked at dilemmas we had faced ourselves. By the end of class, I’d discovered that questions with no right answer can turn out to be the most interesting ones.

The experience has taught me to look at things a little more “philosophically”—and not just because it was a philosophy class! I learned that if I let go of my preconceptions, I can actually get a lot out of subjects I was previously dismissive of. The class taught me—in more ways than one—to look at things with an open mind.

A descriptive essay provides a detailed sensory description of something. Like narrative essays, they allow you to be more creative than most academic writing, but they are more tightly focused than narrative essays. You might describe a specific place or object, rather than telling a whole story.

Descriptive essays test your ability to use language creatively, making striking word choices to convey a memorable picture of what you’re describing.

A descriptive essay can be quite loosely structured, though it should usually begin by introducing the object of your description and end by drawing an overall picture of it. The important thing is to use careful word choices and figurative language to create an original description of your object.

Mouse over the example below, a response to the prompt “Describe a place you love to spend time in,” to learn more about descriptive essays.

On Sunday afternoons I like to spend my time in the garden behind my house. The garden is narrow but long, a corridor of green extending from the back of the house, and I sit on a lawn chair at the far end to read and relax. I am in my small peaceful paradise: the shade of the tree, the feel of the grass on my feet, the gentle activity of the fish in the pond beside me.

My cat crosses the garden nimbly and leaps onto the fence to survey it from above. From his perch he can watch over his little kingdom and keep an eye on the neighbours. He does this until the barking of next door’s dog scares him from his post and he bolts for the cat flap to govern from the safety of the kitchen.

With that, I am left alone with the fish, whose whole world is the pond by my feet. The fish explore the pond every day as if for the first time, prodding and inspecting every stone. I sometimes feel the same about sitting here in the garden; I know the place better than anyone, but whenever I return I still feel compelled to pay attention to all its details and novelties—a new bird perched in the tree, the growth of the grass, and the movement of the insects it shelters…

Sitting out in the garden, I feel serene. I feel at home. And yet I always feel there is more to discover. The bounds of my garden may be small, but there is a whole world contained within it, and it is one I will never get tired of inhabiting.

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Though every essay type tests your writing skills, some essays also test your ability to read carefully and critically. In a textual analysis essay, you don’t just present information on a topic, but closely analyze a text to explain how it achieves certain effects.

Rhetorical analysis

A rhetorical analysis looks at a persuasive text (e.g. a speech, an essay, a political cartoon) in terms of the rhetorical devices it uses, and evaluates their effectiveness.

The goal is not to state whether you agree with the author’s argument but to look at how they have constructed it.

The introduction of a rhetorical analysis presents the text, some background information, and your thesis statement; the body comprises the analysis itself; and the conclusion wraps up your analysis of the text, emphasizing its relevance to broader concerns.

The example below is from a rhetorical analysis of Martin Luther King Jr.’s “I Have a Dream” speech . Mouse over it to learn more.

King’s speech is infused with prophetic language throughout. Even before the famous “dream” part of the speech, King’s language consistently strikes a prophetic tone. He refers to the Lincoln Memorial as a “hallowed spot” and speaks of rising “from the dark and desolate valley of segregation” to “make justice a reality for all of God’s children.” The assumption of this prophetic voice constitutes the text’s strongest ethical appeal; after linking himself with political figures like Lincoln and the Founding Fathers, King’s ethos adopts a distinctly religious tone, recalling Biblical prophets and preachers of change from across history. This adds significant force to his words; standing before an audience of hundreds of thousands, he states not just what the future should be, but what it will be: “The whirlwinds of revolt will continue to shake the foundations of our nation until the bright day of justice emerges.” This warning is almost apocalyptic in tone, though it concludes with the positive image of the “bright day of justice.” The power of King’s rhetoric thus stems not only from the pathos of his vision of a brighter future, but from the ethos of the prophetic voice he adopts in expressing this vision.

Literary analysis

A literary analysis essay presents a close reading of a work of literature—e.g. a poem or novel—to explore the choices made by the author and how they help to convey the text’s theme. It is not simply a book report or a review, but an in-depth interpretation of the text.

Literary analysis looks at things like setting, characters, themes, and figurative language. The goal is to closely analyze what the author conveys and how.

The introduction of a literary analysis essay presents the text and background, and provides your thesis statement; the body consists of close readings of the text with quotations and analysis in support of your argument; and the conclusion emphasizes what your approach tells us about the text.

Mouse over the example below, the introduction to a literary analysis essay on Frankenstein , to learn more.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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At high school and in composition classes at university, you’ll often be told to write a specific type of essay , but you might also just be given prompts.

Look for keywords in these prompts that suggest a certain approach: The word “explain” suggests you should write an expository essay , while the word “describe” implies a descriptive essay . An argumentative essay might be prompted with the word “assess” or “argue.”

The vast majority of essays written at university are some sort of argumentative essay . Almost all academic writing involves building up an argument, though other types of essay might be assigned in composition classes.

Essays can present arguments about all kinds of different topics. For example:

• In a literary analysis essay, you might make an argument for a specific interpretation of a text
• In a history essay, you might present an argument for the importance of a particular event
• In a politics essay, you might argue for the validity of a certain political theory

An argumentative essay tends to be a longer essay involving independent research, and aims to make an original argument about a topic. Its thesis statement makes a contentious claim that must be supported in an objective, evidence-based way.

An expository essay also aims to be objective, but it doesn’t have to make an original argument. Rather, it aims to explain something (e.g., a process or idea) in a clear, concise way. Expository essays are often shorter assignments and rely less on research.

The key difference is that a narrative essay is designed to tell a complete story, while a descriptive essay is meant to convey an intense description of a particular place, object, or concept.

Narrative and descriptive essays both allow you to write more personally and creatively than other kinds of essays , and similar writing skills can apply to both.

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