statistical for research topics

Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

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Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

• Literacy rate in a city.
• Abortion and pregnancy rate in the USA.
• Eating disorders in the citizens.
• Parent role in self-esteem and confidence of the student.
• Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

• Statistical analysis for legs and head injuries in Football.
• Statistical analysis for shoulder and knee injuries in MotoGP.
• Deep statistical evaluation for the doping test in sports from the past decade.
• Statistical observation on the performance of athletes in the last Olympics.
• Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

• Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
• Statistical evolution to find out the suicide reason among students and adults.
• Statistics analysis to find out the effect of divorce on children in a country.
• Psychology affects women because of the gender gap in specific country areas.
• Statistics analysis to find out the cause of online bullying in students’ lives. 
• In Psychology, PTSD and descriptive tendencies are discussed.
• The function of researchers in statistical testing and probability.
• Acceptable significance and probability thresholds in clinical Psychology.
• The utilization of hypothesis and the role of P 0.05 for improved comprehension.
• What types of statistical data are typically rejected in psychology?
• The application of basic statistical principles and reasoning in psychological analysis.
• The role of correlation is when several psychological concepts are at risk.
• Actual case study learning and modeling are used to generate statistical reports.
• In psychology, naturalistic observation is used as a research sample.
• How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

• Does education have a deep impact on the financial success of an individual?
• The investment in digital technology is having a meaningful return for corporations?
• The gap of financial wealth between rich and poor in the USA.
• A statistical approach to identify the effects of high-frequency trading in financial markets.
• Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

• Statistical analysis on the effect of methamphetamine on substance abusers.
• Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
• Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
• Statistics analysis to identify the role of genes in the child’s overall immunity.
• What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

• Generic vs private education is one of the best for the students and has better financial return.
• Psychology vs physiology: which leads the person not to quit their addictions?
• Effect of breastmilk vs packed milk on the infant child overall development
• Which causes more accidents: male alcoholics vs female alcoholics.
• What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

• Application of statistics in the world of data science
• Statistics for finance: how statistics is helping the company to grow their finance
• Advantages and disadvantages of Radar chart
• Minor marriages in south-east Asia and African countries.
• Discussion of ANOVA and correlation.
• What statistical methods are most effective for active sports?
• When measuring the correctness of college tests, a ranking statistical approach is used.
• Statistics play an important role in Data Mining operations.
• The practical application of heat estimation in engineering fields.
• In the field of speech recognition, statistical analysis is used.
• Estimating probiotics: how much time is necessary for an accurate statistical sample?
• How will the United States population grow in the next twenty years?
• The legislation and statistical reports deal with contentious issues.
• The application of empirical entropy approaches with online grammar checking.
• Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

• Uses of statistics in chemometrics
• Statistics in business analytics and business intelligence
• Importance of statistics in physics.
• Deep discussion about multivariate statistics
• Uses of Statistics in machine learning

Survey Topics for Statistics

• Gather the data of the most qualified professionals in a specific area.
• Survey the time wasted by the students in watching Tvs or Netflix.
• Have a survey the fully vaccinated people in the USA 
• Gather information on the effect of a government survey on the life of citizens
• Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

• Have a deep decision of Bayes theorems
• Discuss the Bayesian hierarchical models
• Analysis of the process of Japanese restaurants. 
• Deep analysis of Lévy’s continuity theorem
• Analysis of the principle of maximum entropy

AP Statistics Topics

• Discuss about the importance of econometrics
• Analyze the pros and cons of Probit Model
• Types of probability models and their uses
• Deep discussion of ortho stochastic matrix
• Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

• National income and the regulation of cryptocurrency.
• The benefits and drawbacks of regression analysis.
• How can estimate methods be used to correct statistical differences?
• Mathematical prediction models vs observation tactics.
• In sociology research, there is bias in quantitative data analysis.
• Inferential analytical approaches vs. descriptive statistics.
• How reliable are AI-based methods in statistical analysis?
• The internet news reporting and the fluctuations: statistics reports.
• The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

• Role of statistics in business in 2023
• Importance of business statistics and analytics
• What is the role of central tendency and dispersion in statistics
• Best process of sampling business data.
• Importance of statistics in big data.
• The characteristics of business data sampling: benefits and cons of software solutions.
• How may two different business tasks be tackled concurrently using linear regression analysis?
• In economic data relations, index numbers, random probability, and correctness are all important.
• The advantages of a dataset approach to statistics in programming statistics.
• Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

• Evaluate the role of John Tukey’s contribution to statistics.
• The role of statistics to improve ADHD treatment.
• The uses and timeline of probability in statistics.
• Deep analysis of Gertrude Cox’s experimental design in statistics.
• Discuss about Florence Nightingale in statistics.
• What sorts of music do college students prefer?
• The Main Effect of Different Subjects on Student Performance.
• The Importance of Analytics in Statistics Research.
• The Influence of a Better Student in Class.
• Do extracurricular activities help in the transformation of personalities?
• Backbenchers’ Impact on Class Performance.
• Medication’s Importance in Class Performance.
• Are e-books better than traditional books?
• Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

• Introduction of a problem
• Methodology explanation and choice. 
• Statistical research itself is in the main part (Body Part). 
• Samples deviations and variables. 
• Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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120 Statistical Research Topics: Explore Up-to-date Trends

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

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Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

• Predictive Healthcare Modeling with Machine Learning
• Analyzing Online Education During COVID-19 Epidemic
• Modeling How Climate Change Affects Natural Disasters
• Essential Elements Influencing Personnel Productivity
• Social Media Influence on Customer Choices and Behavior
• Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
• Financial Markets and Stock Price Predictions
• Statistical Analysis of Voting-related Behaviors
• An Analysis of Public Transportation Usage Trends in Urban Areas
• How Can Public Health Education Reduce Air Pollution?
• Statistical Analysis of Suicide In Adolescents and Adults
• A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

• Major Factors Influencing College Students’ Academic Performance
• Social Media and How It Defines thee Mental Health of Students
• Evaluation of the Elements Influencing Student Engagement and Retention
• An Examination of Extracurricular Activities On Academic Success
• Does Parental Involvement Determine Academic Achievement of Kids?
• Examining How Technology Affects Improving Educational Performance
• Factors That Motivate Students’ Involvement In Online Learning
• The Impact of Socioeconomic Status On Academic Performance
• Does Criticism Enhance Student Performance?
• Student-Centered Learning and Improved Performance
• A Cursory Look At Students’ Career Goals and Major Life Decisions
• Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

• Financial Data And Stock Price Forecasting
• Investigation of Variables Influencing Students’ Grades
• What Causes Traffic Flow and Congestion In Urban Areas?
• How to Guarantee Customer Retention In the Retail Sector
• Using Epidemiological Data to Model the Spread of Infectious Diseases
• Does Direct Advertisement Affect Consumer Preferences and Behavior?
• How to Predict and Adapt to Climate Change
• Using Spatial Statistics to Analyze Trends and Patterns In Crime
• Examination of the Elements Influencing Workplace Morale and Productivity
• Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
• How Many Percent Get Married After Their Degree Programs?
• A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

• Analysis of the Variables Affecting A Startup’s Success
• The Valid Connection Between Mental Health and Social Media Use
• Different Teaching Strategies and Academic Performance
• Factors Influencing Employee Satisfaction In Different Work Environments
• The Impact of Public Policy On Different Population Groups
• Reviewing Different Health Outcomes and Incomes
• Different Marketing Tactics for Good Service Promotion
• What Influences Results In Different Sports Competitions?
• Differentiating Elements Affecting Students’ Performance In A Given Subject
• Internal Communication and Building An Effective Workplace
• Does the Use of Business Technologies Boost Workers’ Output?
• The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

• How Global Pandemic Affects Local Businesses
• Investigating the Link Between Income and Health Outcomes In a Demography
• Key Motivators for Student’s Performance In a Particular Academic Program
• Evaluating the Success of a Promotional Plan Over Others
• Continuous Social Media Use and Impact On Mental Health
• Does Culture Impact the Religious Beliefs of Certain Groups?
• Key Indicators of War and How to Manage These Indicators
• An Overview of War As a Money Laundering Scheme
• How Implementations Guarantee Effectiveness of Laws In Rural Areas
• Performance of Students In War-torn Areas
• Key Indicators For Measuring the Success of Your Venture
• How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students  will want to write in include:

• The Most Affected Age Demography By the Covid-19 Pandemic
• The Health Outcomes Peculiar to a Specific Demography
• Unusual Ways to Enhance Student Performance In a Classroom
• How Marketing Efforts Can Determine Promotional Outputs
• Can Mental Health Solutions Be Provided On Social Media?
• Assessing How Certain Species Are Affected By Climate Change.
• What Influences Voter Turnouts In Different Elections?
• How Many People Have Used Physical Exercises to Improve Mental Health
• How Financial Circumstances Can Determine Criminal Activities
• Ways DUI Laws Can Reduce Road Accidents
• Examining the Connection Between Corruption and Underdevelopment In Africa
• What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

• Retail Client Behaviors and Weather Trends
• The Impact of Marketing Initiatives On Sales and Customer Retention
• How Socioeconomic Factors Determine Crime Rates In Different Locations
• Public and Private School Students: Who Performs Better?
• How Fitness Affects the Mental Health of People In Different Ages
• Focus On the Unbanked Employees Globally
• Does Getting Involve In a Kid’s Life Make Them Better?
• Dietary Decisions and a Healthy Life
• Managing Diabetes and High Blood Pressure of a Specific Group
• How to Engage Different Learning Methods for Effectiveness
• Understudying the Sleeping Habits of Specific Age Groups
• How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

• Understanding Consumer Spending and Behavior In Different Regions
• Why Some People in Certain Areas Live Longer than Others
• Comparative Analysis of Different Customer Behaviors
• Do Social Media Businesses Benefit More than Physical Businesses?
• Does a Healthy Work Environment Guarantee Productivity?
• The Impact of Ethnicity and Religion On Voting Patterns
• Does Financial Literacy Guarantee Better Money Management?
• Cultural Identities and Behavioral Patterns
• How Religious Orientation Determines Social Media Use
• The Growing Need for Economists Globally
• Getting Started with Businesses On Social Media
• Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

• Consumer Satisfaction-Related Variables on E-Commerce Websites
• Obesity Rates and Socioeconomic Status In Developed Countries
• How Marketing Strategies Can Make or Mar Sales Performance
• The Correlation Between Increased Income and Happiness In Various Nations
• Regression Models and Forecasting Home Prices
• Climate Change Affecting Agricultural Production In Specific Areas
• A Study of Employee Satisfaction In the Healthcare Industry
• Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
• Predicting the Risk of Default Among Credit Card Holders In Different Regions
• Why Crime Rates Are Increasing In Urban Areas than Rural Areas
• Statistical Evaluation of Methamphetamine’s Impact On Drug Users
• Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

• Social Media’s Effects On Consumer Behavior
• The Correlation Between Urban Crime Rates and Poverty Levels
• Physical Exercise and Mental Health Consequences
• Predictive Modeling In the Financial Markets
• How Minimum Wage Regulations Impact Employment Rates
• Healthcare Outcomes and Access Across Various Socioeconomic Groups
• How High School Students’ Environment Affect Academic Performance
• Automated Technology and Employment Loss
• Environmental Elements and Their Effects On Public Health
• Various Advertising Tactics and How They Influence Customer Behavior
• Political Polarization And Economic Inequality
• Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

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Statistics Research Topics: Ideas & Questions

June 16, 2023

Looking for research topics in statistics? Whether you’re a student working on a class project or a researcher in need of inspiration, finding the right topic can be challenging. With numerous areas to explore in statistics, narrowing down your options can be overwhelming. But with some creativity and research, you can find an interesting and relevant topic. This article offers ideas and examples of statistics research topics to consider, so let’s dive in!

Statistics Research: What It Comprises

The data collected by statistics research can be quantitative (numbers) or qualitative (text). The data can also be presented in tables or graphs for easy understanding by the audience. However, it is not always necessary to present the data in the form of tables or graphs, as sometimes the raw data can be good enough to convey the message from the researcher.

In statistics projects, the researchers usually design experiments to test specific hypotheses about a population’s characteristics or behavior. For example, suppose you want to know whether people who wear glasses will have better eyesight than those who don’t wear glasses. In that case, you need to collect information about their vision before and after wearing glasses (experimental group) and compare their vision with those who do not wear glasses (control group). You would then find out whether there was any difference between these two groups with respect to eyesight improvement due to wearing glasses.

Tips on How to Choose a Statistics Research Topic

Firstly, remember that a good statistics topic should interest you and also have a substantial amount of data available for analysis. Once you have decided on your topic, you can collect data for your study using secondary sources or conducting primary research through surveys or interviews.

You can also use search engines like Google or Yahoo! to find information about your topic of interest. You can use keywords like “income disparity” or “inequality causes” to find relevant websites on which you can find information related to your topic of interest.

Next, consider what types of questions your supervisor would like answered with this data type. For example, if you’re looking at crime rates in your city, maybe they would like to know which areas have higher crime rates than others to plan police patrols accordingly. Or maybe they just want to know whether there’s any correlation between high crime rates and low-income neighborhoods (there probably will be).

Feel free to select any topic and try our free AI essay generator to craft your essay.

Statistics Research Topics in Business

• Understanding the factors that influence consumer purchase decisions in the technology industry
• Advertising and sales revenue: a time-series analysis
• The effectiveness of customer loyalty programs in increasing customer retention and revenue
• The relationship between employee job satisfaction and productivity
• The factors that contribute to employee turnover in the hospitality industry
• Product quality on customer satisfaction and loyalty: a longitudinal study
• The application of social media marketing in increasing brand awareness and customer engagement
• Corporate social responsibility (CSR) initiatives and brand reputation: a meta-analysis
• Understanding the factors that influence customer satisfaction in the restaurant industry
• E-commerce on traditional brick-and-mortar retail sales: a comparative analysis
• The effectiveness of supply chain management strategies in reducing operational costs and improving efficiency
• The relationship between market competition and innovation: a cross-country analysis
• Understanding the factors that influence employee motivation and engagement in the workplace
• Business analytics on strategic decision-making: a case study approach
• The effectiveness of performance-based incentives in increasing employee productivity and job satisfaction
• Organizational performance dependence on employee diversity and organizational performance
• Understanding the factors that contribute to startup success in the technology industry
• The impact of pricing strategies on sales revenue and profitability
• The effectiveness of corporate training programs in improving employee skill development and performance
• The relationship between brand image and customer loyalty

Research Topics in Applied Statistics

• The impact of educational attainment on income level
• The effectiveness of different advertising strategies in increasing sales
• The relationship between socioeconomic status and health outcomes
• The effectiveness of different teaching methods in promoting academic success
• The impact of job training programs on employment rates
• The relationship between crime rates and community demographics
• Different medication dosages in treating a particular condition
• The influence of environmental pollutants on health outcomes
• The interconnection between access to healthcare and health outcomes
• The effectiveness of different weight loss programs in promoting weight loss
• The impact of social support on mental health outcomes
• The relationship between demographic factors and political affiliation
• The effectiveness of different exercise programs in promoting physical fitness
• The influence of parenting styles on child behavior
• The relationship between diet and chronic disease risk
• Different smoking cessation programs for promoting smoking cessation
• The impact of public transportation on urban development
• The relationship between technology usage and social isolation
• The effectiveness of different stress reduction techniques in reducing stress levels
• The influence of climate change on crop

Statistics Research Topics in Psychology

• The correlation between childhood trauma and adult depression
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders
• The impact of social media on self-esteem and body image in adolescents
• Personality traits and job satisfaction: how are they related?
• The prevalence and predictors of bullying in schools
• The effects of sleep deprivation on cognitive performance
• The role of parenting styles in the development of emotional intelligence
• The effectiveness of mindfulness-based interventions in reducing stress and anxiety
• The impact of childhood abuse on adult relationship satisfaction
• The influence of social support on coping with chronic illness
• The factors that contribute to successful aging
• The prevalence and predictors of addiction relapse
• The impact of cultural factors on mental health diagnosis and treatment
• Exercise and mental health: in which way are they connected?
• The effectiveness of art therapy in treating trauma-related disorders
• The prevalence and predictors of eating disorders in college students
• The influence of attachment styles on romantic relationships
• The effectiveness of group therapy in treating substance abuse disorders
• The prevalence and predictors of postpartum depression
• The impact of childhood socioeconomic

Sports Statistics Research Topics

• The relationship between player performance and team success in the National Football League (NFL)
• Understanding the factors that influence home-field advantage in professional soccer
• The impact of game-day weather conditions on player performance in Major League Baseball (MLB)
• The effectiveness of different training regimens in improving endurance and performance in long-distance running
• The relationship between athlete injury history and future injury risk in professional basketball
• The impact of crowd noise on team performance in college football
• The effectiveness of sports psychology interventions in improving athlete performance and mental health
• The relationship between player height and success in professional basketball: a regression analysis
• Understanding the factors that contribute to the development of youth soccer players in the United States
• The influence of playing surface on injury rates in professional football: a longitudinal study
• The effectiveness of pre-game routines in improving athlete performance in tennis
• The relationship between athletic ability and academic success among college athletes
• Understanding the factors that influence injury risk and recovery time in professional hockey players
• The impact of in-game statistics on coaching decisions in professional basketball
• The effectiveness of different dietary regimens in improving athlete performance in endurance sports
• The relationship between athlete sleep habits and performance: a longitudinal study
• Understanding the factors that influence athlete endorsement deals and sponsorships in professional sports
• The influence of stadium design on crowd noise levels and player performance in college football
• The effectiveness of different strength training regimens in improving athlete performance in track and field events
• The relationship between player salary and team success in professional baseball: a longitudinal analysis

Survey Methods Statistics Research Topics

• Understanding the factors that influence response rates in online surveys
• The effectiveness of different survey question formats in eliciting accurate and reliable responses
• The relationship between survey mode (phone, online, mail) and response quality in political polling
• The impact of incentives on survey response rates and data quality
• Understanding the factors that contribute to respondent satisfaction in surveys
• The effectiveness of different sampling methods in achieving representative samples in survey research
• The relationship between survey item order and response bias: a meta-analysis
• The impact of social desirability bias on survey responses: a longitudinal study
• Understanding the factors that influence survey question wording and response bias
• The effectiveness of different visual aids in improving respondent comprehension and response quality
• The relationship between survey timing and response rate: a comparative analysis
• The impact of interviewer characteristics on survey response quality in face-to-face surveys
• Understanding the factors that contribute to nonresponse bias in survey research
• The effectiveness of different response scales in measuring attitudes and perceptions in surveys
• The relationship between survey length and respondent engagement: a cross-sectional analysis
• The influence of skip patterns on survey response quality and completion rates
• Understanding the factors that influence survey item nonresponse and item refusal rates
• The effectiveness of pre-testing and piloting surveys in improving data quality and reliability
• The relationship between survey administration and response quality: a comparative analysis of phone, online, and in-person surveys
• The impact of survey fatigue on response quality and data completeness: a longitudinal study

As mentioned above, statistics is the science of collecting and analyzing data to draw conclusions and make predictions. To conduct a proper statistical analysis, you must first define your research question, gather data from various sources, analyze the information, and draw conclusions based on the results.

This process can be challenging for many people who do not have an extensive background in statistics. However, it does not have to be so tricky if you use our professional Custom Writing help. Our writers are highly qualified professionals who will work with you to develop a clear understanding of your research problem and then guide you through every step of the process. We will also ensure that your paper follows all academic standards to meet all requirements for originality and quality.

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Inferential Statistics | An Easy Introduction & Examples

Published on September 4, 2020 by Pritha Bhandari . Revised on June 22, 2023.

While descriptive statistics summarize the characteristics of a data set, inferential statistics help you come to conclusions and make predictions based on your data.

When you have collected data from a sample , you can use inferential statistics to understand the larger population from which the sample is taken.

Inferential statistics have two main uses:

• making estimates about populations (for example, the mean SAT score of all 11th graders in the US).
• testing hypotheses to draw conclusions about populations (for example, the relationship between SAT scores and family income).

Table of contents

Descriptive versus inferential statistics, estimating population parameters from sample statistics, hypothesis testing, other interesting articles, frequently asked questions about inferential statistics.

Descriptive statistics allow you to describe a data set, while inferential statistics allow you to make inferences based on a data set.

• Descriptive statistics

Using descriptive statistics, you can report characteristics of your data:

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

In descriptive statistics, there is no uncertainty – the statistics precisely describe the data that you collected. If you collect data from an entire population, you can directly compare these descriptive statistics to those from other populations.

Inferential statistics

Most of the time, you can only acquire data from samples, because it is too difficult or expensive to collect data from the whole population that you’re interested in.

While descriptive statistics can only summarize a sample’s characteristics, inferential statistics use your sample to make reasonable guesses about the larger population.

With inferential statistics, it’s important to use random and unbiased sampling methods . If your sample isn’t representative of your population, then you can’t make valid statistical inferences or generalize .

Sampling error in inferential statistics

Since the size of a sample is always smaller than the size of the population, some of the population isn’t captured by sample data. This creates sampling error , which is the difference between the true population values (called parameters) and the measured sample values (called statistics).

Sampling error arises any time you use a sample, even if your sample is random and unbiased. For this reason, there is always some uncertainty in inferential statistics. However, using probability sampling methods reduces this uncertainty.

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The characteristics of samples and populations are described by numbers called statistics and parameters :

• A statistic is a measure that describes the sample (e.g., sample mean ).
• A parameter is a measure that describes the whole population (e.g., population mean).

Sampling error is the difference between a parameter and a corresponding statistic. Since in most cases you don’t know the real population parameter, you can use inferential statistics to estimate these parameters in a way that takes sampling error into account.

There are two important types of estimates you can make about the population: point estimates and interval estimates .

• A point estimate is a single value estimate of a parameter. For instance, a sample mean is a point estimate of a population mean.
• An interval estimate gives you a range of values where the parameter is expected to lie. A confidence interval is the most common type of interval estimate.

Both types of estimates are important for gathering a clear idea of where a parameter is likely to lie.

Confidence intervals

A confidence interval uses the variability around a statistic to come up with an interval estimate for a parameter. Confidence intervals are useful for estimating parameters because they take sampling error into account.

While a point estimate gives you a precise value for the parameter you are interested in, a confidence interval tells you the uncertainty of the point estimate. They are best used in combination with each other.

Each confidence interval is associated with a confidence level. A confidence level tells you the probability (in percentage) of the interval containing the parameter estimate if you repeat the study again.

A 95% confidence interval means that if you repeat your study with a new sample in exactly the same way 100 times, you can expect your estimate to lie within the specified range of values 95 times.

Although you can say that your estimate will lie within the interval a certain percentage of the time, you cannot say for sure that the actual population parameter will. That’s because you can’t know the true value of the population parameter without collecting data from the full population.

However, with random sampling and a suitable sample size, you can reasonably expect your confidence interval to contain the parameter a certain percentage of the time.

Your point estimate of the population mean paid vacation days is the sample mean of 19 paid vacation days.

Hypothesis testing is a formal process of statistical analysis using inferential statistics. The goal of hypothesis testing is to compare populations or assess relationships between variables using samples.

Hypotheses , or predictions, are tested using statistical tests . Statistical tests also estimate sampling errors so that valid inferences can be made.

Statistical tests can be parametric or non-parametric. Parametric tests are considered more statistically powerful because they are more likely to detect an effect if one exists.

Parametric tests make assumptions that include the following:

• the population that the sample comes from follows a normal distribution of scores
• the sample size is large enough to represent the population
• the variances , a measure of variability , of each group being compared are similar

When your data violates any of these assumptions, non-parametric tests are more suitable. Non-parametric tests are called “distribution-free tests” because they don’t assume anything about the distribution of the population data.

Statistical tests come in three forms: tests of comparison, correlation or regression.

Comparison tests

Comparison tests assess whether there are differences in means, medians or rankings of scores of two or more groups.

To decide which test suits your aim, consider whether your data meets the conditions necessary for parametric tests, the number of samples, and the levels of measurement of your variables.

Means can only be found for interval or ratio data , while medians and rankings are more appropriate measures for ordinal data .

Correlation tests

Correlation tests determine the extent to which two variables are associated.

Although Pearson’s r is the most statistically powerful test, Spearman’s r is appropriate for interval and ratio variables when the data doesn’t follow a normal distribution.

The chi square test of independence is the only test that can be used with nominal variables.

Regression tests

Regression tests demonstrate whether changes in predictor variables cause changes in an outcome variable. You can decide which regression test to use based on the number and types of variables you have as predictors and outcomes.

Most of the commonly used regression tests are parametric. If your data is not normally distributed, you can perform data transformations.

Data transformations help you make your data normally distributed using mathematical operations, like taking the square root of each value.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Confidence interval
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

• 1.1 AP Statistics Topics for Project
• 1.2 Statistics Project Topics for High School Students
• 1.3 Statistical Survey Topics
• 1.4 Statistical Experiment Ideas
• 1.5 Easy Stats Project Ideas
• 1.6 Business Ideas for Statistics Project
• 1.7 Socio-Economic Easy Statistics Project Ideas
• 1.8 Experiment Ideas for Statistics and Analysis
• 2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

• Challenging concepts suitable for advanced placement courses.
• Accessible ideas that are engaging and educational for younger students.
• Ideas for conducting surveys and analyzing the results.
• Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

Need help with statistics project? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

AP Statistics Topics for Project

• Analyzing Variance in Climate Data Over Decades.
• The Correlation Between Economic Indicators and Standard of Living.
• Statistical Analysis of Voter Behavior Patterns.
• Probability Models in Sports: Predicting Outcomes.
• The Effectiveness of Different Teaching Methods: A Statistical Study.
• Analysis of Demographic Data in Public Health.
• Time Series Analysis of Stock Market Trends.
• Investigating the Impact of Social Media on Academic Performance.
• Survival Analysis in Clinical Trial Data.
• Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

• The Mathematics of Personal Finance: Budgeting and Spending Habits.
• Analysis of Class Performance: Test Scores and Study Habits.
• A Statistical Comparison of Local Public Transportation Options.
• Survey on Dietary Habits and Physical Health Among Teenagers.
• Analyzing the Popularity of Various Music Genres in School.
• The Impact of Sleep on Academic Performance: A Statistical Approach.
• Statistical Study on the Use of Technology in Education.
• Comparing Athletic Performance Across Different Sports.
• Trends in Social Media Usage Among High School Students.
• The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

• Public Opinion on Environmental Conservation Efforts.
• Consumer Preferences in the Fast Food Industry.
• Attitudes Towards Online Learning vs. Traditional Classroom Learning.
• Survey on Workplace Satisfaction and Productivity.
• Public Health: Attitudes Towards Vaccination.
• Trends in Mobile Phone Usage and Preferences.
• Community Response to Local Government Policies.
• Consumer Behavior in Online vs. Offline Shopping.
• Perceptions of Public Safety and Law Enforcement.
• Social Media Influence on Political Opinions.

Statistical Experiment Ideas

• The Effect of Light on Plant Growth.
• Memory Retention: Visual vs. Auditory Information.
• Caffeine Consumption and Cognitive Performance.
• The Impact of Exercise on Stress Levels.
• Testing the Efficacy of Natural vs. Chemical Fertilizers.
• The Influence of Color on Mood and Perception.
• Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
• The Effectiveness of Different Types of Water Filters.
• Analyzing the Impact of Room Temperature on Concentration.
• Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

• Average Daily Screen Time Among Students.
• Analyzing the Most Common Birth Months.
• Favorite School Subjects Among Peers.
• Average Time Spent on Homework Weekly.
• Frequency of Public Transport Usage.
• Comparison of Pet Ownership in the Community.
• Favorite Types of Movies or TV Shows.
• Daily Water Consumption Habits.
• Common Breakfast Choices and Their Nutritional Value.
• Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

• Analyzing Customer Satisfaction in Retail Stores.
• Market Analysis of a New Product Launch.
• Employee Performance Metrics and Organizational Success.
• Sales Data Analysis for E-commerce Websites.
• Impact of Advertising on Consumer Buying Behavior.
• Analysis of Supply Chain Efficiency.
• Customer Loyalty and Retention Strategies.
• Trend Analysis in Social Media Marketing.
• Financial Risk Assessment in Investment Decisions.
• Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

• Income Inequality and Its Impact on Education.
• The Correlation Between Unemployment Rates and Crime Levels.
• Analyzing the Effects of Minimum Wage Changes.
• The Relationship Between Public Health Expenditure and Population Health.
• Demographic Analysis of Housing Affordability.
• The Impact of Immigration on Local Economies.
• Analysis of Gender Pay Gap in Different Industries.
• Statistical Study of Homelessness Causes and Solutions.
• Education Levels and Their Impact on Job Opportunities.
• Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

• Multivariate Analysis of Global Climate Change Data.
• Time-Series Analysis in Predicting Economic Recessions.
• Logistic Regression in Medical Outcome Prediction.
• Machine Learning Applications in Statistical Modeling.
• Network Analysis in Social Media Data.
• Bayesian Analysis of Scientific Research Data.
• The Use of Factor Analysis in Psychology Studies.
• Spatial Data Analysis in Geographic Information Systems (GIS).
• Predictive Analysis in Customer Relationship Management (CRM).
• Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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Best stats research topics in 2023: Innovations in Statistical Analysis

Are you searching for the best stats research topics in 2023? If yes, then have a close look at some of the best stats research topics in 2023.

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is an essential tool for understanding and making sense of complex data sets and is used in a wide range of fields, including business, healthcare, social sciences, and engineering.

Research in statistics is critical for developing new statistical methods, improving existing techniques, and applying statistical analysis to solve real-world problems. This paper will provide an overview of some of the key research topics in statistics, including data analysis, experimental design, statistical modeling, machine learning, and data visualization.

Each section will define the topic, provide an overview of the current state of research, and discuss future directions for research in that area. By providing an overview of these key topics, this paper aims to highlight the importance of ongoing research in statistics and to inspire future research in this critical field.

Importance of Statistics Research

Table of Contents

Have a close look at the importance of statistics research.

Helps in making informed decisions

Statistics research helps individuals, businesses, and governments make informed decisions based on data-driven analysis. For example, businesses can use statistical analysis to identify consumer trends, predict demand for products, and make decisions about marketing strategies. Governments can use statistical analysis to evaluate the effectiveness of policies, allocate resources, and make decisions about public services.

Improves accuracy

Statistics research helps to improve the accuracy of predictions, forecasts, and estimations. By analyzing large datasets, researchers can identify patterns and trends that may not be immediately visible. This can lead to more accurate predictions about future outcomes, which can be useful in many fields, from finance to healthcare.

Provides insights

Statistics research can provide insights into the relationships between variables and the factors that drive certain outcomes. For example, by analyzing data on consumer behavior, researchers can identify which factors influence purchasing decisions, and use this information to develop more effective marketing strategies.

Validates hypotheses

Statistics research can be used to test hypotheses and validate theories in various fields. For example, in the field of psychology, researchers can use statistical analysis to test the effectiveness of different therapy techniques and validate theories about human behavior.

Enables evidence-based policymaking

Statistics research can provide evidence to support policymaking and guide public policy decisions. For example, by analyzing data on crime rates, policymakers can identify areas where crime is most prevalent and develop policies to address the issue.

Assists in risk management

Statistics research can be used to assess risks and identify potential threats in various contexts. For example, in the field of finance, statistical analysis can be used to assess the risk associated with different investment strategies.

Enhances research and development

Statistics research can help to enhance research and development efforts by providing valuable insights and feedback. For example, in the field of medicine, statistical analysis can be used to evaluate the effectiveness of new drugs and treatments.

Supports quality improvement

Statistics research can support quality improvement efforts by identifying areas of improvement and measuring the effectiveness of interventions. For example, in the field of education, statistical analysis can be used to evaluate the effectiveness of different teaching methods and identify areas where improvements can be made.

Facilitates performance measurement

Statistics research can be used to measure performance in various contexts, such as business, healthcare, and education. For example, in the field of business, statistical analysis can be used to measure employee performance and identify areas where improvements can be made.

Helps in predicting future trends

Statistics research can be used to analyze past trends and make predictions about future trends and outcomes. For example, in the field of finance, statistical analysis can be used to make predictions about stock market trends and identify investment opportunities.

Stats Research Topics

Have a close look at stats research topics.

Health and Medicine

Correlation between diet and health outcomes.

The study of the correlation between diet and health outcomes is an important topic in health and medicine statistics research. With the rise of chronic diseases such as obesity, diabetes, and heart disease, there is a growing need to understand the relationship between diet and health outcomes. Researchers can use statistics to analyze large datasets and identify patterns and correlations between dietary habits and health outcomes. This analysis can lead to the development of effective interventions and policies to improve dietary habits and prevent chronic diseases.

Effectiveness of various medications

The effectiveness of various medications is another important topic in health and medicine statistics research. With new medications constantly being developed, it is important to evaluate their effectiveness in treating various conditions. Researchers can use statistics to analyze clinical trial data to determine the effectiveness of different medications, and to identify any side effects or risks associated with their use. This analysis can lead to the development of better medications and improved treatment protocols.

Analysis of vaccination rates and their impact on public health

Vaccinations are an important tool in preventing the spread of infectious diseases, but there is often controversy surrounding their use. Statistics research can be used to analyze vaccination rates and their impact on public health, including the reduction of disease outbreaks and healthcare costs associated with treating these diseases. This analysis can lead to the development of effective vaccination policies and programs to improve public health outcomes.

Study of healthcare utilization and costs

The study of healthcare utilization and costs is another important topic in health and medicine statistics research. Researchers can use statistics to analyze healthcare utilization patterns, including hospital admissions, emergency department visits, and physician visits. This analysis can help identify areas where healthcare resources are being overused or underused, and can inform the development of policies and interventions to improve healthcare utilization and reduce costs.

Analysis of health disparities

Health disparities refer to differences in health outcomes between different groups of people. Statistics research can be used to analyze health disparities and identify the factors that contribute to them. This analysis can help inform the development of interventions and policies to reduce health disparities and improve health outcomes for all populations.

Study of environmental health

Environmental health is an important topic in health and medicine statistics research. Researchers can use statistics to analyze the relationship between environmental exposures and health outcomes. This analysis can help inform the development of policies and interventions to reduce environmental exposures and improve public health outcomes.

Overall, statistics research plays a crucial role in the field of health and medicine, allowing researchers to better understand the relationships between diet, medications, vaccinations, healthcare utilization, health disparities, and environmental health. By using statistical methods to analyze large datasets, researchers can identify patterns and correlations that can lead to improved healthcare practices and better public health outcomes.

Social Sciences

Social sciences deal with the study of human society and relationships between individuals and groups. Here are some potential research topics related to social sciences:

Here are some potential statistics research topics in the field of social sciences:

Study of crime rates and factors that contribute to criminal behavior

Researchers can use statistical methods to analyze crime rates and identify the factors that contribute to criminal behavior, such as poverty, unemployment, and education levels. This information can help policymakers develop effective strategies to prevent crime and improve public safety.

Analysis of income inequality and its effects on society

Income inequality is a pressing social issue that can have far-reaching impacts on society, such as increased crime rates and decreased social mobility. Researchers can use statistical methods to analyze income inequality trends and their impacts on various aspects of society, such as healthcare, education, and employment.

Impact of various forms of media on social attitudes and behaviors

Social media, television, and other forms of media have the power to shape social attitudes and behaviors. Researchers can use statistical methods to analyze the effects of different forms of media on issues such as political polarization, racial attitudes, and mental health.

Study of the effects of education on income and social mobility

Education is often seen as a key factor in promoting social mobility and reducing income inequality. Researchers can use statistical methods to analyze the relationship between education levels and income, and to identify the factors that influence this relationship.

Analysis of the effects of immigration on society

Immigration is a complex issue that can have significant impacts on society, such as changes in demographics, economic growth, and cultural norms. Researchers can use statistical methods to analyze the effects of immigration on various aspects of society, such as crime rates, healthcare, and labor markets.

Study of the effects of social policies on vulnerable populations: Social policies such as welfare programs and healthcare reforms are designed to help vulnerable populations, but their effectiveness can vary widely. Researchers can use statistical methods to analyze the impacts of social policies on different populations, such as low-income families, the elderly, and individuals with disabilities.

Here are some potential statistics research topics in economics:

Economic impacts of COVID-19 pandemic

The COVID-19 pandemic has had significant impacts on the global economy, with wide-ranging effects on different industries, countries, and demographic groups. Statistics research can be used to analyze the economic impacts of the pandemic, including changes in employment rates, consumer spending, and GDP.

Analysis of stock market trends and investment strategies

The stock market is a complex and dynamic system that can be difficult to predict. Statistics research can be used to analyze stock market trends and identify potential investment strategies, such as diversification, value investing, and growth investing.

Relationship between minimum wage and economic growth

The minimum wage is a controversial topic in economics, with proponents arguing that it can stimulate economic growth by increasing consumer spending and reducing poverty, while opponents argue that it can lead to job losses and inflation. Statistics research can be used to analyze the relationship between minimum wage and economic growth, including the effects on employment rates, inflation, and GDP.

Analysis of international trade patterns

International trade is a critical component of the global economy, with significant impacts on different countries and industries. Statistics research can be used to analyze international trade patterns, including the factors that drive trade flows, the effects on economic growth and development, and the implications for global economic governance.

Evaluation of economic policies

Governments and international organizations often implement economic policies aimed at promoting economic growth, reducing inequality, and mitigating economic crises. Statistics research can be used to evaluate the effectiveness of these policies, including the impacts on different sectors of the economy and the distributional effects on different demographic groups.

Analysis of income inequality

Income inequality is a growing concern in many countries, with significant implications for social welfare and economic development. Statistics research can be used to analyze income inequality patterns, including the factors that contribute to income disparities, the effects on different demographic groups, and the implications for economic growth and development.

Overall, statistics research can be a powerful tool for analyzing complex economic phenomena and developing evidence-based policies and strategies to promote economic growth, reduce inequality, and mitigate economic crises.

Environment and Sustainability

Here are some potential statistics research topics in the field of Environment and Sustainability:

Analysis of climate change impacts on agriculture

Climate change can have significant impacts on agricultural productivity, including changes in temperature, rainfall patterns, and extreme weather events. Researchers can use statistical analysis to understand the relationships between climate variables and agricultural outcomes, and to develop strategies to adapt to and mitigate the impacts of climate change on food systems.

Evaluation of the effectiveness of carbon pricing policies

Carbon pricing policies such as carbon taxes and emissions trading systems are increasingly being implemented as a means of reducing greenhouse gas emissions. Researchers can use statistical methods to evaluate the effectiveness of these policies in reducing emissions and achieving other environmental goals, such as promoting the transition to renewable energy sources .

Analysis of water resource management strategies

Water is a critical resource for human well-being and ecosystem health, and effective management is essential for sustainability. Researchers can use statistical analysis to evaluate the effectiveness of different water resource management strategies, such as water conservation programs and watershed management plans, and to identify areas for improvement.

Assessment of the environmental impacts of transportation systems

Transportation is a significant contributor to greenhouse gas emissions and air pollution. Researchers can use statistical methods to analyze the environmental impacts of different transportation modes, such as cars, buses, trains, and airplanes, and to evaluate the effectiveness of policies to promote sustainable transportation.

Evaluation of sustainable land use practices

Land use change is a major driver of biodiversity loss, deforestation, and soil degradation. Researchers can use statistical analysis to evaluate the effectiveness of different sustainable land use practices, such as agroforestry, conservation agriculture, and reforestation, in promoting biodiversity conservation and ecosystem health.

Overall, statistics research is essential for understanding the complex relationships between human activities and the natural environment, and for developing effective strategies for promoting sustainability and mitigating environmental impacts.

Technology-related statistics research

Technology-related statistics research is an important field of study that involves analyzing data related to technological advancements and their impact on society. Some potential research topics in this field include:

Analysis of cybersecurity threats

With the increasing reliance on technology in various industries, cybersecurity threats have become a major concern. Statistics research can be used to analyze patterns and trends in cyber attacks, and to develop strategies to mitigate the risks associated with them.

Evaluation of technology adoption rates

The adoption of new technologies can have a significant impact on businesses and society as a whole. Statistics research can be used to analyze adoption rates of new technologies, and to identify factors that influence their adoption.

Study of the impact of technology on employment

Advances in technology have led to significant changes in the job market, with some jobs becoming obsolete and new jobs emerging. Statistics research can be used to analyze the impact of technology on employment, and to develop strategies to mitigate the negative effects.

Analysis of social media trends

Social media has become an integral part of modern society, with billions of users around the world. Statistics research can be used to analyze social media trends and behaviors, and to develop strategies for using social media effectively.

Study of the impact of artificial intelligence (AI)

AI is becoming increasingly prevalent in various industries, from healthcare to finance. Statistics research can be used to analyze the impact of AI on these industries, and to identify potential risks and benefits.

Evaluation of technology-related policies

Governments around the world have implemented various policies related to technology, such as net neutrality and data privacy regulations. Statistics research can be used to evaluate the effectiveness of these policies and to identify areas for improvement.

Overall, technology-related statistics research is an important field of study that can help us better understand the impact of technology on society and develop strategies for using technology effectively and responsibly.

In conclusion, statistics research plays a crucial role in various fields such as health, social sciences, economics, and the environment. The potential research topics in each of these fields are vast, ranging from the correlation between diet and health outcomes to the analysis of climate change and its effects on ecosystems. The findings of statistics research can have significant implications for decision-makers in various industries, policymakers, and society as a whole. Therefore, it is vital to continue exploring and studying statistics research to gain a deeper understanding of the world around us.

Frequently Asked Questions

What is statistics research.

Statistics research involves the collection, analysis, and interpretation of numerical data to derive insights and make informed decisions. It is used in various fields to study trends, patterns, and relationships in data.

Why is statistics research important?

Statistics research helps us make informed decisions based on data, rather than relying on assumptions or guesswork. It allows us to study complex phenomena, identify patterns and trends, and test hypotheses. It is used in many fields, including healthcare, social sciences, economics, and environmental studies.

What are some common statistical methods used in research?

Some common statistical methods used in research include regression analysis, hypothesis testing, data visualization, and time series analysis. The choice of method depends on the research question and the type of data being analyzed.

How can statistics research benefit society?

Statistics research can benefit society in many ways, such as identifying factors that contribute to public health issues, evaluating the effectiveness of social policies, and predicting economic trends. It can also inform decision-making in industries such as healthcare, education, and environmental conservation.

What are some potential limitations of statistics research?

Some potential limitations of statistics research include the possibility of sampling bias, errors in data collection, and confounding variables that may affect the results. It is important to carefully design studies and use appropriate statistical methods to minimize these limitations.

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Top 100 Statistics Topics To Research In 2023

If you are looking for some interesting statistics topics that should work well in 2023, you have arrived at the right place. We have a list of 100 awesome statistics topics that you can use to get the inspiration you need. And did you know that all our statistics topics for project and statistics paper topics are 100% free? You can use them as you like and even reword them.

The Importance of a Good Statistics Topic

Why would you need our statistics project topics list? What makes a good statistics topic so important? The truth is that professors are subjective when it comes to essays and topics. Most of them will award bonus points to students who manage to come up with interesting statistics project topic ideas. After all, a great topic means you’ve invested a lot of time and effort into the paper, studied popular and scholarly sources to write it. We know that original statistics project topics are hard to come by, so we’ve created a list of 100 brand new topics for 2023.

Statistics Projects Topics

Our ENL writers compiled a list of the most common statistics projects topics. You can easily write an essay on these in one or two days because they don’t require much research:

• Using statistics in actuarial science
• Analyze an example of statistical signal processing
• Compare the Smith chart and the Sankey diagram
• Discuss the correlation coefficient
• Practical application of the Metropolis-Hastings algorithm
• Getting ready for a world of robots

Easy Statistics Research Topics

We have a list of easy statistics research topics that you can surely handle all by yourself. Choose one of these topics and start writing:

• Using statistics in epidemiology
• Applications of statistical physics
• Pros and cons of the Stemplot and Radar chart
• Using a Venn diagram correctly
• Child marriages in Africa (statistics)
• Discuss the analysis of variance (ANOVA) process
• Discuss the Box–Jenkins method

Statistical Research Topic for High School

Are you a high school student who needs to find a great statistics idea for an essay? Check out the following statistical research topic for high school:

• Using statistics in chemometrics
• Statistics and business analytics
• Discuss the field of statistical thermodynamics
• Principal component analysis in multivariate statistics
• What is a kernel density estimation?
• Selecting the correct sample for a survey
• What are cross-sectional studies?

Most Interesting Topics in Statistics

We’ve included all of the most interesting topics in statistics in a separate list. You can find the best of the best right here:

• Using statistics in machine learning
• What are statistical finance processes?
• Statistics in quality control in 2023
• Compare and contrast the Skewplot and the Sparkline
• Using Renkonen similarity index in botanic studies
• Calculate the probability of success using the binomial proportion confidence interval
• Statistics as a mathematical science

Hot Topics for Statistics Projects

Some ideas are better than others, especially when it comes to finding a good topic. Here are what we consider to be very hot topics for statistics projects:

• Using statistics in jurimetrics
• What are environmental statistics?
• Compare the curve fitting and smoothing processes
• Analyze 3 GEEs (Generalized estimating equations)
• Discuss the Rule of three in medicine
• The Goodman and Kruskal’s lambda measure

Survey Topics for Statistics

Conducting a survey is not that difficult, we agree. However, finding a good topic for your survey is. Pick one of our survey topics for statistics and start organizing the survey in minutes:

• Gather information about the GPA from 70 students in your university
• Survey how much time students spend doing their homework
• Make a survey on surveys
• Make a survey about the English language in high school
• What is your favorite city survey
• What do you think about our government survey
• Are you satisfied with your life survey

Good Topics for Statistics Projects

This is the list where you can find the topics that are not breathtaking. Check out these good topics for statistics projects and select one today:

• Analyze the Markov Chain central limit theorem
• Discuss the loop-erased random walk model
• Bernoulli matrix vs the Centering matrix in statistics
• Using statistics in psychometrics
• Interpreting the total sum of squares correctly
• Apply Kuder–Richardson’s Formula 20 in psychometrics

AP Statistics Topics

Advanced Placement Statistics is one of the most difficult courses for college students. This is why we want to help you with some very interesting AP statistics topics:

• Getting an adjacency matrix quickly
• What is the orthostochastic matrix?
• Obtaining the transition matrix optimally
• Discuss econometrics and its role
• Analyze the pros of the Probit Model
• Categorical data analysis and the Cochran–Armitage test for trend
• The history of probability

Theoretical Statistics Topics for a Core Course

If you are looking for some nice theoretical statistics topics for a core course, you have arrived at the right place. Here are some of our best ideas:

• Advantages of the Ornstein–Uhlenbeck process
• Discuss the Malliavin stochastic calculus
• Discuss stochastic optimal control
• Discuss homoscedasticity and heteroscedasticity
• Predicting errors using the Akaike information criterion
• The history of statistics

Business Statistics Topics

Would you like to write about business? Our experienced team of writers and editors managed to come up with these original business statistics topics:

• The importance of statistics to business in 2023
• Kinds of data in business statistics
• Measures of central tendency and dispersion
• Discuss inferential statistics
• The process of sampling business data
• Effective uses of statistics in key business decisions
• The effects of probability on business decisions

Good Statistics Projects Topics

We know you want to keep things fresh and get some bonus points for an interesting topic. Here are some very good statistics projects topics that should work great in 2023:

• Statistics and the medical treatment of drug addiction
• How did Nate Silver predict the outcome of the 2008 US election?
• Describe the information theory in statistics
• How does AI use the Fuzzy associative matrix?
• Composing a questionnaire the right way
• Effects of questions on interviewees
• The importance of the order of questions in a survey

Statistical Research Topics for College Students

Of course, we have plenty of statistical research topics for college students. These are more difficult than those for high school students, but they should be manageable:

• Analyze John Tukey’s contribution to statistics
• Florence Nightingale and visual representation in statistics
• Discuss Gertrude Cox’s experimental design in statistics
• How does statistics improve ADHD treatment?
• The Krichevsky–Trofimov estimator in information theory
• The timeline of probability in statistics
• Discuss Pseudorandomness and Quasirandomness

Controversial Topics for Statistics Project

Just like any field, statistics has its fair share of controversial topics. We managed to gather the most intriguing controversial topics for statistics project right here:

• Should we pursue the artificial neural network?
• Using the Attack Rate statistic during an epidemic
• Discuss the ”admissible decision” rule
• The link between statistics and biometrics
• Should we abandon null hypothesis significance testing?
• Is the Bayes theorem incorrect?

Statistics Research Paper Topics for Graduates

We have a list of statistics research paper topics for graduates, of course. You can get some very nice ideas from these examples:

• Discuss Bayesian hierarchical models
• Discuss basic AJD (basic affine jump diffusion)
• A thorough analysis of Lévy’s continuity theorem
• Analyze the Chinese restaurant process
• The Cochran–Mantel–Haenszel test
• A practical analysis of the principle of maximum entropy
• An in-depth look at the Hewitt–Savage Zero–One law

Difficult Statistical Research Topics

If you want to try your hand at a more difficult topic, we can help. Take a quick look at these difficult statistical research topics and choose the one you like:

• Statistics and the science of probability
• Organizing neurobiological time series data
• Analyzing intrinsic fluctuations in biochemical systems
• Effective data mining of neurophysiological biomarkers
• Econometrics and statistics
• Discuss the axioms of probability (Kolmogorov)

Do you think these statistical project topics are not enough to get you a top grade? If you want an awesome statistics project topic, don’t hesitate to contact us. We will think of some unique topics and send them your way right away. Also, we can do much more than just create statistical projects topics. If you need assignment help , editing or proofreading assistance, we are the company to call. We have extensive experience writing essays and term papers for students of all ages. Our PhD writers are ready to spring into action and make sure you turn in an awesome essay – on time!

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Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Introduction to Statistics

(15 reviews)

David Lane, Rice University

Copyright Year: 2003

Publisher: David Lane

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Terri Torres, professor, Oregon Institute of Technology on 8/17/23

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics... read more

Comprehensiveness rating: 5 see less

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics rather than too few.

Content Accuracy rating: 5

Yes, Lane is both thorough and accurate.

Relevance/Longevity rating: 5

What is covered is what is usually covered in an introductory statistics book. The only topic I may, given sufficient time, cover is bootstrapping.

Clarity rating: 5

The book is clear and well-written. For the trickier topics, simulations are included to help with understanding.

Consistency rating: 5

All is organized in a way that is consistent with the previous topic.

Modularity rating: 5

The text is organized in a way that easily enables navigation.

Organization/Structure/Flow rating: 5

The text is organized like most statistics texts.

Interface rating: 5

Easy navigation.

Grammatical Errors rating: 5

I didn't see any grammatical errors.

Cultural Relevance rating: 5

Nothing is included that is culturally insensitive.

The videos that accompany this text are short and easy to watch and understand. Videos should be short enough to teach, but not so long that they are tiresome. This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot. In addition, Lane has promised to send an instructor's manual and slide deck.

Reviewed by Professor Sandberg, Professor, Framingham State University on 6/29/21

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful. read more

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.

I did not find any errors.

Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.

I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.

Even with different authors of chapter, the writing is consistent.

The text is well organized into sections making it easy to assign individual topics and sections.

The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.

I had no problem navigating the text online.

The writing is grammatical correct.

I saw no issues that would be offensive.

I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.

Reviewed by Emilio Vazquez, Associate Professor, Trine University on 4/23/21

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming. read more

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming.

I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.

Some of the examples may need updating depending on the times but the examples are still relevant at this time.

This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.

The text is consistent throughout using the same verbiage in various sections.

The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.

The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing

I had no problems with navigation

All textbooks have a few errors but certainly nothing glaring or making text difficult

I saw no issues and I am part of a cultural minority in the US

Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.

Reviewed by Audrey Hickert, Assistant Professor, Southern Illinois University Carbondale on 3/29/21

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and... read more

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.

I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).

Relevance/Longevity rating: 4

Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.

Clarity rating: 4

All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses "M" for mean, rather than x-bar.

The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.

Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.

The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.

Interface rating: 4

For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.

The text is easy to read and free from any obvious grammatical errors.

Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.

This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.

Reviewed by Shahar Boneh, Professor, Metropolitan State University of Denver on 3/26/21, updated 4/22/21

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course. read more

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course.

The text seems to be statistically accurate.

It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.

It is written clearly.

Consistency rating: 4

The terminology is fairly consistent. There is room for some improvement.

By the nature of the subject, the topics have to be presented in a sequential and coherent order. However, the book breaks things down quite effectively.

Organization/Structure/Flow rating: 3

Some of the topics are interleaved and not presented in the order I would like to cover them.

Good interface.

The grammar is ok.

The book seems to be culturally neutral, and not offensive in any way.

I really liked the simulations that go with the book. Parts of the book are a little too advanced for students who are learning statistics for the first time.

Reviewed by Julie Gray, Adjunct Assistant Professor, University of Texas at Arlington on 2/26/21

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by... read more

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by Dr. Lane and colleagues. It is obvious that several iterations have only made it better.

I found all the material accurate.

Essentially, statistical concepts at the introductory level are accepted as universal. This suggests that the relevance of this textbook will continue for a long time.

The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.

Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.

Modularity rating: 4

The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.

The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author. It is wonderful to have access to these helpful resources for instructors to use in preparation.

I did not find any interface issues.

The book is well written. In my reading I did not notice grammatical errors.

For this subject and in the examples given, I did not notice any cultural issues.

For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not. Preparing future social workers by teaching introductory statistics is essential to meet that goal.

Reviewed by Mamata Marme, Assistant Professor, Augustana College on 6/25/19

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables... read more

Comprehensiveness rating: 4 see less

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables attached in the same package, even though they are available online.

The terminology and notation used in the textbook is pretty standard. The content is accurate.

The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions.

The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion. What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult. There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry. It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.

The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered. The end of chapter problems and the statistical literacy applications are closely tied to the material covered.

The authors have done a good job treating each chapter as if they stand alone. The lack of connection to a past reference may create a sense of disconnect between the topics discussed

The text's "modularity" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section. The earlier material is easy to find but not consistently referred to in the text.

I had no problem with the interface. The online version is more visually interesting than the pdf version.

I did not see any grammatical errors.

Cultural Relevance rating: 4

I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology.

Overall, this textbook will cover most of the topics in a survey of statistics course.

Reviewed by Alexandra Verkhovtseva, Professor, Anoka-Ramsey Community College on 6/3/19

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range... read more

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range of intro stats topics (and some more), plus the case studies and the glossary.

The content is pretty accurate, I did not find any biases or errors.

The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies.

The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny.

Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book is covered in good detail.

The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course.

The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical.

The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.

No grammatical errors found.

The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text.

In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students.

Reviewed by Dabrina Dutcher, Assistant Professor, Bucknell University on 3/4/19

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for... read more

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections. Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas. The probability sections do not include any significant combinatorics work, which is sometimes included at this level.

I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly. The examples and problems still feel relevant and reasonably modern. My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators. As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.

Solid. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature. It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.

I did not detect any problems with inconsistent symbol use or switches in terminology.

Modularity rating: 3

This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Different books still use different variable symbols even for basic calculated statistics. So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students.

However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.

This book uses a very standard order for the material. The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.

There are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference.

I did not detect any problems.

I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though

I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references. However, I did not see anything offensive or biased in the book.

Reviewed by Ilgin Sager, Assistant Professor, University of Missouri - St. Louis on 1/14/19

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics.... read more

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course.

It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.

This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.

The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student.

The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.

When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.

The text presents the information in a logical order.

The learner can easily follow up the material; there is no interface problem.

There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.

Not applicable for this textbook.

Reviewed by Suhwon Lee, Associate Teaching Professor, University of Missouri on 6/19/18

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises,... read more

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.

Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.

The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.

The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.

Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.

The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.

Organization/Structure/Flow rating: 4

Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.

The text is free of any issues. There are no navigation problems nor any display issues.

The text contains no grammatical errors.

The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.

Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.

Reviewed by Jenna Kowalski, Mathematics Instructor, Anoka-Ramsey Community College on 3/27/18

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. read more

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks.

Content Accuracy rating: 3

The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination. These examples should be corrected to reflect current values of inclusive teaching.

The text contains relevant information that is current and will not become outdated in the near future. The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.

The text is very clear and direct with the language used. The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing. Graphs, tables, and visual displays are clearly labeled.

The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.

The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.

The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted. Each module is well-defined.

Adding an option of returning to the previous page would be of great value to the reader. While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.

No grammatical errors were found while reviewing select pages of this text at random.

Cultural Relevance rating: 3

Several examples contained data that were not formally cited. These examples need to be corrected to reflect current inclusive teaching strategies. For example, one question stated that “while men are XX times more likely to commit murder than women, …” This data should be cited, otherwise the information can be interpreted as biased and offensive.

An included solutions manual for the exercises would be valuable to educators who choose to use this text.

Reviewed by Zaki Kuruppalil, Associate Professor, Ohio University on 2/1/18

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the... read more

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.

The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.

The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.

The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.

Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters.

The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.

Presentation of the topics are well thought and are presented in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.

No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.

Grammatical Errors rating: 4

Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. For X = 1, Y' = (0.425)(1) + 0.785 = 1.21. For X = 2, Y' = (0.425)(2) + 0.785 = 1.64.

However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: Statistics include numerical facts and figures. For instance: • The largest earthquake measured 9.2 on the Richter scale. • Men are at least 10 times more likely than women to commit murder. • One in every 8 South Africans is HIV positive. • By the year 2020, there will be 15 people aged 65 and over for every new baby born.

Solutions for the exercises would be a great teaching resource to have

Reviewed by Randy Vander Wal, Professor, The Pennsylvania State University on 2/1/18

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module... read more

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)

The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.

The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.

The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.

This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.

The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module. It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics.

Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.

Each chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.

The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X.

The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.

This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation.

The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.

This text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.

Reviewed by David jabon, Associate Professor, DePaul University on 8/15/17

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary. read more

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary.

The material is completely accurate. There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread." "Interquartile range" is simply a better, more descriptive term of the concept that it describes. It is also more commonly used nowadays.

This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added.

The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).

The text is internally consistent with one exception that I noted (the use of the synonymous words "H-spread" and "interquartile range").

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text.

The organization, in particular the ordering of the topics, is rather standard with a few exceptions. Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r). I have tried this approach, and I now like this approach. But it is a very challenging chapter for students.

The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements.

The book contains no grammatical errors.

The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text. This text is very high quality textbook in every way.

Table of Contents

• 1. Introduction
• 2. Graphing Distributions
• 3. Summarizing Distributions
• 4. Describing Bivariate Data
• 5. Probability
• 6. Research Design
• 7. Normal Distributions
• 8. Advanced Graphs
• 9. Sampling Distributions
• 10. Estimation
• 11. Logic of Hypothesis Testing
• 12. Testing Means
• 14. Regression
• 15. Analysis of Variance
• 16. Transformations
• 17. Chi Square
• 18. Distribution-Free Tests
• 19. Effect Size
• 20. Case Studies
• 21. Glossary

Ancillary Material

• Ancillary materials are available by contacting the author or publisher .

About the Book

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

About the Contributors

David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University.

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Evaluating the Robustness and Ruggedness of a Statistical Model for Comparison of Mass Spectral Data for Seized Drug Identification

This paper addresses the increasing challenges of identifying novel psychoactive substances based on visual evaluation of spectra in gas chromatography-mass spectrometry; it lays out a summary of the research project, including major goals and objectives, research design, methods, and data analysis, outcomes, limitations, artifacts, references cited, and five appendices.

The authors report on their efforts to evaluate the robustness and ruggedness of the statistical comparison method for compounds representing different novel psychoactive substance (NPS) classes. They suggest that the statistical method described and demonstrated in this report will provide an objective method to statistically compare mass spectra, and note that the method is an extension of current methods to compare spectra, with a statistical evaluation of the reference spectrum and the sample spectrum rather than a visual assessment. For their evaluation, the authors selected compounds representing different NPS classes including structural and positional isomers that were previously documented as being difficult to distinguish based on electron-ionization (EI) mass spectra. The four specific research were: to access the effect of sample concentration on statistical association and discrimination of positional isomers (robustness); to assess the effect of different instruments on statistical association and discrimination of positional isomers (ruggedness); to develop and implement testing of the statistical comparison method in operational forensic science laboratories (testing); and to develop and host training sessions to provide recommendations for implementing the method in forensic laboratories (training). The report describes the authors’ research design, methods, and data analysis techniques. Outcomes are discussed in detail, and descriptions are provided of the six data sets that were generated by the project. The appendices include: Instrument Parameters; Association and Discrimination of Synthetic Cathinones; Association and Discrimination of Fentanyl Analogs; Association and Discrimination of Synthetic Cannabinoids; and Association and Discrimination of Fluoroisobutyryl Fentanyl (FIBF) Position Isomers.

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9 facts about u.s. catholics.

Catholics are one of the largest religious groups in the United States, outnumbering any single Protestant denomination. The U.S. has more Catholics than all but three other countries – Brazil, Mexico and the Philippines – according to the Vatican’s Statistical Yearbook of the Church.

Here are nine key facts about the U.S. Catholic population.

Pew Research Center conducted this analysis to serve as a reference source on the basic demographic attributes, religious characteristics and political preferences of U.S. Catholics.

The analysis draws mainly on data from the Center’s National Public Opinion Reference Surveys (NPORS) conducted in 2022 and 2023. NPORS is an annual survey of U.S. adults who are selected for participation using address-based sampling from the U.S. Postal Service’s Delivery Sequence File. Respondents may answer either by paper or online.

This post also relies on a variety of surveys conducted online among respondents who are part of the Center’s American Trends Panel or other national survey panels recruited through random sampling (not “opt-in” polls).

All of the surveys used in this analysis are weighted to be representative of the U.S. adult population by gender, race, ethnicity, education and other categories.

Today, 20% of U.S. adults describe themselves as Catholics, according to our latest survey. This percentage has been generally stable since 2014. But it is slightly lower than in 2007, when 24% of U.S. adults identified as Catholic.

Overall, there were about 262 million adults in the U.S. in 2023, according to the U.S. Census Bureau . This suggests that there are roughly 52 million Catholic adults nationwide.

Most U.S. Catholics are White, but a third are Hispanic. The Catholic population is 57% White, 33% Hispanic, 4% Asian and 2% Black, while 3% are of another race.

Since 2007, the share of U.S. Catholics who are White has dropped by 8 percentage points, while the share who are Hispanic has ticked upward by 4 points.

This change has implications for the profile of Catholic Americans as a whole because White Catholics have distinctive social and political traits, as we’ll discuss in more detail below.

Catholics tend to be older than Americans overall. Nearly six-in-ten Catholic adults (58%) are ages 50 and older. Among all U.S. adults in the survey, by comparison, 48% fall in this age range.

But Hispanic Catholics tend to be a lot younger than White Catholics. Fewer than half of Hispanic Catholics (43%) are 50 and older, compared with about two-thirds (68%) of White Catholics. And just 14% of Hispanic Catholics are ages 65 and older, versus 38% of White Catholics.

Roughly three-in-ten U.S. Catholics (29%) live in the South, while 26% live in the Northeast, 24% in the West and 21% in the Midwest.

The racial and ethnic profile of the Catholic population varies considerably by region. For example, in the Midwest, 80% of Catholics are White and 17% are Hispanic. In the Northeast, 72% of Catholics are White and 19% are Hispanic.

In the South, 49% are White and 40% are Hispanic. And in the West, there are more Hispanic Catholics than White Catholics (55% vs. 30%).

About a third of U.S. Catholics (32%) have a bachelor’s degree. Another 28% have some college experience but not a bachelor’s degree, and 40% have a high school education or less. This distribution is similar to that of the general adult population.

On average, White Catholics have higher levels of educational attainment than Hispanic Catholics. Roughly four-in-ten White Catholics (39%) have at least a bachelor’s degree, while 32% have a high school education or less. Among Hispanic Catholics, 16% have a bachelor’s degree and 59% have a high school education or less.

About three-in-ten U.S. Catholics (28%) say they attend Mass weekly or more often. Larger shares of Catholics say they pray on a daily basis (52%) and say religion is very important in their life (46%).

Overall, 20% of U.S. Catholics say they attend Mass weekly and pray daily and consider religion very important in their life. By contrast, 10% of Catholics say they attend Mass a few times a year or less often and pray seldom or never and consider religion not too or not all important in their life.

By way of comparison, 40% of U.S. Protestants say they attend services at least weekly. And about two-thirds of Protestants pray daily (67%) and say religion is very important in their life (66%).

About half of Catholic registered voters (52%) identify with or lean toward the Republican Party, while 44% affiliate with the Democratic Party.

But partisan affiliation varies by race and ethnicity. Roughly six-in-ten White Catholic registered voters (61%) say they identify with or lean toward the GOP, compared with 35% of Hispanic Catholics.

Conversely, 60% of Hispanic Catholics who are registered voters say they identify with or lean toward the Democratic Party, compared with 37% of White Catholics.

In the 2022 congressional midterm elections, 56% of Catholics said they voted for Republican candidates, while 43% backed Democrats. And in the 2020 presidential election, Catholic voters were split down the middle: 49% backed Donald Trump and 50% voted for Joe Biden.

These overall splits hide big differences between White and Hispanic Catholic voters. For example, White Catholics favored Trump over Biden by a 15-point margin in 2020, while Hispanic Catholics backed Biden over Trump by a 35-point margin.

While the Catholic Church opposes abortion , about six-in-ten Catholics say abortion should be legal. This includes 39% who say it should be legal in most cases and 22% who say it should be legal in all cases. Roughly four-in-ten Catholics say abortion should be illegal in most (28%) or all (11%) cases.

Catholics’ opinions about abortion tend to align with their political leanings. Among Catholic Democrats, 78% say abortion should be legal in most or all cases. Among Catholic Republicans, 43% say this.

Catholic Democrats are a little less likely than non-Catholic Democrats to say abortion should be legal in most or all cases (78% vs. 86%).

Three-quarters of Catholics view Pope Francis favorably, according to our February 2024 survey. That’s a little lower than the 80% of Catholics or more who expressed a positive view of Francis in many previous polls during his papacy, which began in 2013.

Catholic Democrats (89%) are much more likely than Catholic Republicans (63%) to view Francis favorably.

Note: This is an update of a post originally published Sept. 4, 2018.

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Under Pope Francis, the College of Cardinals has become less European

Two-thirds of u.s. catholics unaware of pope’s new restrictions on traditional latin mass, many catholics in latin america – including a majority in brazil – support allowing priests to marry, just one-third of u.s. catholics agree with their church that eucharist is body, blood of christ, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Despite High Potential, 75 Vulnerable Economies Face ‘Historic Reversal’

In Half of IDA Countries, Income Gap with Wealthiest Economies is Widening

WASHINGTON, April 15, 2024 — Despite their high potential to advance global prosperity, one-half of the world’s 75 most vulnerable countries are facing a widening income gap with the wealthiest economies for the first time in this century, a new World Bank report has found. Taking full advantage of their younger populations, their rich natural resources, and their abundant solar-energy potential can help them overcome the setback.

The report, The Great Reversal: Prospects, Risks, and Policies in International Development Association Countries , offers the first comprehensive look at the opportunities and risks confronting the 75 countries eligible for grants and zero to low-interest loans from the World Bank’s International Development Association (IDA). These countries are home to a quarter of humanity—1.9 billion people. At a time when populations are aging nearly everywhere else, IDA countries will enjoy a growing share of young workers through 2070—a huge potential “demographic dividend.” These countries are also rich in natural resources, enjoy high potential for solar-energy generation, and boast a large reservoir of mineral deposits that could be crucial for the world’s transition to clean energy.

Yet a historic reversal is underway for them. Over 2020-24, average per capita incomes in half of IDA countries—the largest share since the start of this century—have been growing more slowly than those of wealthy economies. This is widening the income gap between these two groups of countries. One out of three IDA countries is poorer, on average, than it was on the eve of the COVID-19 pandemic. The extreme-poverty rate is more than eight times the average in the rest of the world: one in four people in IDA countries struggles on less than $2.15 a day. These countries now account for 90% of all people facing hunger or malnutrition. Half of these countries are either in debt distress or at high risk of it. Still, except for the World Bank Group and other multilateral development donors, foreign lenders—private as well as government creditors—have been backing away from them.

“The world cannot afford to turn its back on IDA countries,” said Indermit Gill, the World Bank Group’s Chief Economist and Senior Vice President . “The welfare of these countries has always been crucial to the long-term outlook for global prosperity. Three of the world’s economic powerhouses today—China, India, and South Korea—were all once IDA borrowers. All three prospered in ways that whittled down extreme poverty and raised living standards. With help from abroad, today’s batch of IDA countries has the potential to do the same.”

More than half of all IDA countries—39 in all—are in Sub-Saharan Africa. Fourteen of them—mainly small island states—are in East Asia, and eight are in Latin America and the Caribbean. In South Asia, all countries except for India are IDA countries. Thirty-one IDA countries have per capita incomes of less than $1,315 a year. Thirty-three are fragile and conflict-affected states.

IDA countries share similar opportunities. The “demographic dividend”—a deep and growing reserve of young workers—is one of them. Abundant natural resources is another. These countries account for about 20% of global production of tin, copper, and gold. In addition, some IDA countries possess critical mineral deposits essential for the global energy transition. Because of their abundant sunshine, most IDA countries are well situated to take advantage of solar energy. On average, their long-term daily solar-electricity generation potential is among the highest in the world.

This potential, however, comes with risks that must be managed. To reap the demographic dividend, IDA governments will need to undertake policies to improve education and health outcomes and make sure that jobs are available for the rising number of young people who will enter the workforce in the coming decades. To seize the full potential of their natural-resource wealth, IDA countries will need to improve policy frameworks and build stronger institutions capable of better economic management. All of this will require ambitious domestic policy reforms—and significant financial support from the international community.

“IDA countries have incredible potential to deliver strong, sustainable, and inclusive growth. Realizing this potential will require them to implement an ambitious set of policies centered on boosting investment,” said Ayhan Kose, the World Bank’s Deputy Chief Economist and Director of the Prospects Group . “ This means improving fiscal, monetary, and financial policy frameworks and advancing an array of structural reforms to strengthen institutions and enhance human capital."

IDA countries today have large investment needs. In the poorest of them, closing existing development and infrastructure gaps and building resilience to climate change will require investment that amounts to nearly 10% of GDP. The costs of climate disasters have doubled in IDA countries over the past decade: Economic losses from natural disasters average 1.3% of GDP a year—four times the average of other emerging market and developing economies. Such needs will require IDA countries to generate sustained investment booms—the type that boosts productivity and incomes and reduces poverty. Historically, such investment booms have often been sparked by a comprehensive package of policy measures—to bolster fiscal and monetary frameworks, ramp up cross-border trade and financial flows, and improve the quality of institutions. Such reforms are never easy, the report notes. They need careful sequencing and implementation. But previous IDA countries have shown they are possible.

IDA countries will need significant international financial support to make progress and lower the risk of “protracted stagnation,” the report notes. Stronger cooperation on global policy issues—including fighting climate change, facilitating more timely and effective debt restructurings, and supporting cross-border trade and investment—will also be crucial to help IDA countries avert a lost decade in development.

Website: https://www.worldbank.org/en/research/publication/prospects-risks-and-policies-in-IDA-countries

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Women in Tech Statistics: Despite Great Strides, Challenges Persist

Sadly, the tech sector lags behind the rest of the job market when it comes to hiring women. Although women make up nearly half of the U.S. workforce, they only made up roughly a third of the workforces at large global technology firms in 2022. 

Below are more statistics on women in technology that explore the issue further and reveal what steps tech companies can take to level the playing field.   

Statistics on the Low Number of Women in Technology   

Women want to be a part of the workforce, as reflected in their national labor force participation rate of 56.8 percent in 2022. However, they continue to face hurdles when attempting to gain a foothold in the tech industry, reducing the number of women in technology. 

Women Candidates Are Less Likely to Receive Callbacks 

A 2019 study found that women on average are 30 percent less likely than their male counterparts to receive a callback for a job interview. This trend shows how it’s difficult for women just to get on the radars of tech recruiters , even if they meet all the job requirements.

Women of Marginalized Groups Struggle to Break Into Computing-Related Roles

Between 2007 and 2020, women occupied 25 percent of computing-related roles , and white women made up 13 percent of this group. Asian and Pacific Islander women made up 7 percent, Black women made up 3 percent and Latina and Hispanic women made up 2 percent. These uneven percentages signal that women of marginalized groups often experience discrimination that encompasses both race and gender, placing more obstacles in the paths of their tech careers.

Percentage of Women in STEM Fields Remains Low 

Although the number of women in science, technology, engineering and math positions has steadily increased (they made up only 8 percent of STEM roles in 1970), women still only represent about 27 percent of the STEM workforce today. This jump is an encouraging sign for aspiring women in tech, but it also shows there’s still a long way to go in making sure the tech and STEM industries reflect the general workforce.  

Not Enough Women Earn STEM-Related Degrees

Women college graduates dominate in fields like the social sciences, but they only receive 21 percent of computer science degrees, 24 percent of engineering degrees and 24 percent of physics degrees. This means that recruiters have a limited talent pool of women to draw from and that female college students may get discouraged by the small numbers of women in tech sectors, creating a cycle that exacerbates the issue of not enough women entering tech. 

Statistics on Challenges in the Workplace for Women in Tech

The struggles for women in technology continue long after they’ve navigated recruiting and hiring processes. Women are sometimes not perceived as belonging in tech spaces, and this reality impacts both their psychological and economic well-being. 

Women Continue to Be Paid Less Than Their Male CoWorkers

In 2022, women made 82 percent of men’s salaries , only a 2 percent increase since 2002. This stat could reflect women and men entering different sectors, but it could also reveal that women get paid less for performing similar work to their male coworkers. Either way, women are undervalued for their work, and they may struggle to remain in industries like tech without livable and equitable wages.  

Black and Hispanic Women Earn the Lowest Incomes in STEM

A 2021 Pew Research Center study found that Black and Hispanic women receive the lowest earnings among women in STEM fields. Even as companies address pay differences between men and women, these initiatives may overlook women of color. Taking into account race and ethnicity is crucial to developing equitable compensation practices that value the work of all women in tech.

Women of Color Face Racial and Gender Discrimination in the Workplace

Among women of color in the workplace , 17 percent of Asian women, 16 percent of Latina women and 13 percent of Black women report that others make assumptions about their nationality or culture. This contrasts with only 2 percent of white women reporting similar experiences. 

Women in Tech Are More Likely to Be Laid Off Than Men 

Women are 65 percent more likely than men to be impacted by layoffs in the tech industry , which may result in women feeling more pressure to perform at a high level. The frequent loss of women coworkers also takes away chances for women in tech to form closer relationships with each other and expand their professional networks .

Stats About Women in Tech and Leadership Numbers

Leadership positions remain out of reach for many women in technology. And for those women who do land leadership roles, many often encounter others’ doubts about their leadership abilities.  

Women Entrepreneurs Are Outnumbered by Their Male Counterparts

Women started nearly half of U.S. businesses in 2021. But in 2022, male business owners outnumbered women entrepreneurs three to one.   

Women Entrepreneurs of Color Are Severely Underrepresented

Among women-led small businesses , 78.4 percent of business owners identify as white;  11.3 percent identify as Black or African-American; 4.6 percent identify as Hispanic, Latino or Spanish origin; 4.6 percent identify as Asian or Asian-American; and 1 percent identify as Middle Eastern or North African. These numbers may indicate that women of color have less access to resources and opportunities than white women. 

Venture Capital Funding Fails to Reach Women Founders 

Out of the total venture capital raised in 2022 by startups, women-founded companies received only 2.1 percent of funding, which reflects the idea that women founders may lack the reputation and connections that men founders possess in the startup world.

Women Leaders Often Experience Imposter Syndrome

About 75 percent of women in executive positions experience imposter syndrome , meaning they believe they’ve reached their positions by chance or other factors, rather than hard work and talent.

Statistics on Gender Expectations for Women in Technology

In addition to a lack of resources and opportunities, women are often expected to make the most of the few chances they get in tech while shouldering the expectations of traditional gender roles.  

Working Women Are Expected to Shoulder Caregiving Duties

Working women are five to eight times more likely than working men to be affected by caregiving duties . Needing to take time off to care for loved ones forces women to play catch up with their job responsibilities. If tech companies fail to take this reality into account, they may only worsen women employees’ mental health and accelerate employee burnout .  

Women Are Funneled Into Certain Sectors  

According to the U.S. Department of Labor , 96.8 percent of preschool and kindergarten teachers and 91.3 percent of licensed practical and licensed vocational nurses are women. The disproportionate number of women in these professions suggests that traditional gender norms can influence women’s career paths and steer them away from technical roles. 

Women Leaders Are Likely to Be Mistaken for More Junior Workers

Women leaders are twice as likely than their male counterparts to be mistaken as an employee in a more junior role , which suggests that many employees and companies still don’t associate women with high-level positions.

Women Are Less Likely to Be Promoted Than Men

A study from an MIT Sloan professor found that women are 14 percent less likely to be promoted than men. This statistic reinforces the idea that women are overlooked for higher-level opportunities, stunting their professional development and limiting their career options.  

Women Leave Due to Lack of Advancement Opportunities and Poor Work-Life Balance 

Twenty-two percent of women listed a lack of advancement opportunities as the main reason for leaving their companies , while 18 percent reported a lack of work-life balance. Tech companies that fail to provide women with professional development opportunities and comprehensive employee benefits risk losing top women employees to businesses and sectors that account for their personal and professional needs.

Stats About Women in Tech Pushing for Change and Equity 

The deck may be stacked against women in tech, but that hasn’t stopped them from pushing for change and more opportunities. On a global and national level, women have experienced slow but steady progress in the workplace. 

More Women Are Entering Leadership Positions Globally

The number of women hired for leadership positions globally rose from 33.3 percent in 2016 to 36.9 percent in 2022. While the difference might seem small, this statistic shows that women have been reaching the highest levels of tech and other industries in spite of gender biases, unfair expectations and limited company support.  

More Women Are Becoming Entrepreneurs and Opening Businesses Globally

In a study of 50 countries, two out of every five early-stage entrepreneurs were women. Men still outnumber women as business owners and entrepreneurs, but the stat reaffirms that the ranks of women entrepreneurs are rising across the world.

Women in the U.S. Are Owning More Businesses

Within the U.S., women now own 42 percent of all businesses . The global increases in women owners reflect a similar trend happening on a national level, although the numbers may vary between countries.

More Black Women Are Starting Businesses 

In 2021, 17 percent of Black women owned businesses , compared to just 10 percent of white women and 15 percent of white men. Unfortunately, this trend may suggest that Black women are leaving companies where they’ve faced gender and racial discrimination. But more Black women-owned businesses could also signal a chance for Black women to form professional communities and support up-and-coming Black women leaders in tech and other industries.

Companies Can Do More to Support Women in Technology 

While women have demonstrated resilience and success on their own, companies can accelerate their progress by taking concrete steps to support women in tech. Below are a few strategies and policies businesses can implement to enable their women employees to thrive.

Prioritize Diversity Policies in Hiring and Advancement 

Because the tech industry moves so fast,  diversity sometimes gets overlooked — especially when it comes to hiring. One way companies can increase the number of women in their ranks is by adjusting their hiring practices. For example, blind applications can increase women’s chances of being hired by 25 percent.   

Hiring for traits like curiosity, engagement, drive, passion and insight — rather than prior experience alone — enables women to move past the “broken rung” and into entry-level positions that match their qualifications.

Another solution is to establish quotas for accepting women interviewees and hiring women candidates. Women are often overlooked for managerial positions because there are fewer of them in the workplace. With more women hired for entry-level positions, more women can also then be considered for promotions to managerial spots. 

Having a diversity of opinions on tech company boards is also incredibly important for holding companies accountable to their DEI commitments . Ensuring women make up half of the board increases the number of voices calling for equitable hiring and employment practices for women in tech workplaces.

Establish Unconscious Bias Training for Hiring Managers and Employees

Unconscious biases are the underlying attitudes and stereotypes people associate with a person or groups of people. An earlier statistic of women being mistaken for more junior employees highlights how subtle yet pervasive unconscious biases can be. 

These biases also arise in the hiring process when teams hire for culture fit, which gauges how well candidates get along with team members and whether they share similar interests.

Unconscious bias training programs are designed to expose people to their biases and to provide the necessary thought exercises and tools to counteract those behaviors. Methods like counter-stereotyping and perspective-taking expose trainees to women’s negative experiences through reading essays and performing tasks that display the challenges faced by women at work. These trainings develop empathy for women among trainees and can lead to a more welcoming and aware workforce that lifts up women and members of other marginalized groups.

Recognize and Reward Women Leaders and Employees for Their Work

While women are two times more likely than men to work on DEI initiatives , 40 percent of women leaders claim their DEI work goes unacknowledged . Studies have also shown that managers more quickly forget the achievements and statements of Black women than those of white men and white women. 

To build a culture where women are recognized, managers, coworkers and executives must constantly call out instances where a woman’s hard work is going unnoticed. An easy way to show praise and bring unnoticed work to light is to send out frequent emails or internal messages to your group highlighting ideas and projects brought about by women. This way, women’s contributions are on full display and can set women up for future promotions due to their demonstrated impact and leadership qualities .  

Provide Flexible and Remote Work Policies 

Over half of women say working from home has made them more productive , compared to 37 percent of men who say the same thing. In addition, women are two times more likely than men to say working from home has helped with their job advancement. Considering that women often have caregiving and home responsibilities, companies can develop more flexible PTO and work-from-home policies that allow women to balance personal and professional demands.

Businesses can take this approach further by offering wellness days , which give employees the benefit of taking off without the stigma of using personal days. These days can stave off employee burnout, especially among women. Companies that also limit email hours, establish set meeting times and designate days as no-meeting days can avoid an “always-on” culture and boost the mental health of women and the rest of the workforce.

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