inductive and deductive essay examples

• Academic Writing / Online Writing Instruction

Inductive vs. Deductive Writing

by Purdue Global Academic Success Center and Writing Center · Published February 25, 2015 · Updated February 24, 2015

Dr. Tamara Fudge, Kaplan University professor in the School of Business and IT

There are several ways to present information when writing, including those that employ inductive and deductive reasoning . The difference can be stated simply:

• Inductive reasoning presents facts and then wraps them up with a conclusion .
• Deductive reasoning presents a thesis statement and then provides supportive facts or examples.

Which should the writer use? It depends on content, the intended audience , and your overall purpose .

If you want your audience to discover new things with you , then inductive writing might make sense.   Here is n example:

My dog Max wants to chase every non-human living creature he sees, whether it is the cats in the house or rabbits and squirrels in the backyard. Sources indicate that this is a behavior typical of Jack Russell terriers. While Max is a mixed breed dog, he is approximately the same size and has many of the typical markings of a Jack Russell. From these facts along with his behaviors, we surmise that Max is indeed at least part Jack Russell terrier.

Within that short paragraph, you learned about Max’s manners and a little about what he might look like, and then the concluding sentence connected these ideas together. This kind of writing often keeps the reader’s attention, as he or she must read all the pieces of the puzzle before they are connected.

Purposes for this kind of writing include creative writing and perhaps some persuasive essays, although much academic work is done in deductive form.

If your audience is not likely going to read the entire written piece, then deductive reasoning might make more sense, as the reader can look for what he or she wants by quickly scanning first sentences of each paragraph. Here is an example:

My backyard is in dire need of cleaning and new landscaping. The Kentucky bluegrass that was planted there five years ago has been all but replaced by Creeping Charlie, a particularly invasive weed. The stone steps leading to the house are in some disrepair, and there are some slats missing from the fence. Perennials were planted three years ago, but the moles and rabbits destroyed many of the bulbs, so we no longer have flowers in the spring.

The reader knows from the very first sentence that the backyard is a mess! This paragraph could have ended with a clarifying conclusion sentence; while it might be considered redundant to do so, the scientific community tends to work through deductive reasoning by providing (1) a premise or argument – which could also be called a thesis statement, (2) then evidence to support the premise, and (3) finally the conclusion.

Purposes for this kind of writing include business letters and project documents, where the client is more likely to skim the work for generalities or to hunt for only the parts that are important to him or her. Again, scientific writing tends to follow this format as well, and research papers greatly benefit from deductive writing.

Whether one method or another is chosen, there are some other important considerations. First, it is important that the facts/evidence be true. Perform research carefully and from appropriate sources; make sure ideas are cited properly. You might need to avoid absolute words such as “always,” “never,” and “only,” because they exclude any anomalies. Try not to write questions: the writer’s job is to provide answers instead. Lastly, avoid quotes in thesis statements or conclusions, because they are not your own words – and thus undermine your authority as the paper writer.

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Inductive Essay Examples

Unlike in a deductive essay, inductive texts explore the topic without arguing for the correctness of the hypothesis. Here you will provide evidence first and suggest your reasoning only in the concluding paragraph. In terms of structure, you move from the particular cases to the general principle.

Induction essay examples are exciting to read for the “suspense:” which inference will the author make in the end? The method is helpful in complex research topics when it is almost impossible to produce a hypothesis before the analysis.

For an inductive essay sample, look through the links below. All of these texts fall under this genre of academic writing.

20 Best Inductive Essay Examples

Response to hurricane disasters.

• Subjects: Disasters Environment
• Words: 1450

Scientific Method in Fire Investigations

• Subjects: Criminal Investigation Law

Taxation Law: UK Inheritance Tax

• Subjects: Law Taxation Law
• Words: 4286

Science Meets Real Life: Studying Student Absence

• Subjects: Sciences Scientific Method
• Words: 1645

Personal Financing Planning Analysis

• Subjects: Economics Regulation of Finance

Walking a Guest: Hospitality Industry Fraud

• Subjects: Business & Corporate Law Law
• Words: 1393

Nurse Role in the Healthcare Provision

• Subjects: Health & Medicine Nursing

Psychological Theories and Tests of Motivation

• Subjects: Applications of Psychology Psychology

Critical Thinking in Everyday Life

• Subjects: Everyday Interactions Sociology

Leadership Style Self-Assessment

• Subjects: Curriculum Development Education

What It Means to Be a Californian

• Subjects: Identity Sociology

Judy’s Hospital: Patient Admission Process Improvement

• Subjects: Health & Medicine Healthcare Institution
• Words: 1386

Affirmative Action: Achieving Race Equality in School Admissions

• Subjects: Government Politics & Government
• Words: 1489

Good Samaritan Interpretation and Application

• Subjects: Religion Religious Writings

Biblical Living Water Explained

• Subjects: Religion Religious Education

Wire Walking’s History and Examples

• Subjects: Athletes Sports
• Words: 1202

Role of Parents in Physical Education and Sport

• Subjects: Education Education Theories
• Words: 1090

Range of Strategies and Success of the Business

• Subjects: Business Strategy
• Words: 1954

Plato: Piety and Holiness in “Euthyphro”

• Subjects: Philosophical Theories Philosophy

The Single Effect in Edgar Allan Poe’s The Cask of Amontillado

• Subjects: American Literature Literature

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Guide To Inductive & Deductive Reasoning

Induction vs. Deduction

October 15, 2008, by The Critical Thinking Co. Staff

Induction and deduction are pervasive elements in critical thinking. They are also somewhat misunderstood terms. Arguments based on experience or observation are best expressed inductively , while arguments based on laws or rules are best expressed deductively . Most arguments are mainly inductive. In fact, inductive reasoning usually comes much more naturally to us than deductive reasoning.

Inductive reasoning moves from specific details and observations (typically of nature) to the more general underlying principles or process that explains them (e.g., Newton's Law of Gravity). It is open-ended and exploratory, especially at the beginning. The premises of an inductive argument are believed to support the conclusion, but do not ensure it. Thus, the conclusion of an induction is regarded as a hypothesis. In the Inductive method, also called the scientific method , observation of nature is the authority.

In contrast, deductive reasoning typically moves from general truths to specific conclusions. It opens with an expansive explanation (statements known or believed to be true) and continues with predictions for specific observations supporting it. Deductive reasoning is narrow in nature and is concerned with testing or confirming a hypothesis. It is dependent on its premises. For example, a false premise can lead to a false result, and inconclusive premises will also yield an inconclusive conclusion. Deductive reasoning leads to a confirmation (or not) of our original theories. It guarantees the correctness of a conclusion. Logic is the authority in the deductive method.

If you can strengthen your argument or hypothesis by adding another piece of information, you are using inductive reasoning. If you cannot improve your argument by adding more evidence, you are employing deductive reasoning.

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3.3: Inductive and Deductive Reasoning

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Inductive & Deductive Reasoning

Section Learning Objectives

• Define and differentiate between inductive and deductive reasoning, understanding their unique processes and applications in forming logical conclusions.
• Evaluate the strength and validity of arguments using inductive and deductive methods, recognizing the role of premises, conclusions, and the reliability of evidence in each.
• Develop and construct well-reasoned arguments using both inductive and deductive reasoning techniques, applying these methods in academic writing and critical analysis.

Introduction to Inductive vs. Deductive Reasoning

Inductive and deductive reasoning are fundamental approaches in critical thinking, reading, and writing. Understanding the differences between them is crucial for constructing and evaluating arguments effectively.

Inductive reasoning involves making generalizations based on specific observations or evidence. It is also known as informal logic. This approach starts with specific instances and draws broader conclusions. For example, observing that "all observed swans are white" may lead to the general conclusion that "all swans are white." Inductive reasoning is often used in scientific research and everyday decision-making, but it can be prone to errors if the observations are limited or biased. This is why inductive reasoning is considered either strong (most likely true) or weak (fallacious).

Deductive reasoning , on the other hand, begins with a general premise or principle and derives specific conclusions from it. It is also known as formal logic. This method is more rigid and structured, ensuring that if the premises are true, the conclusion must also be true. For example, starting with the general statement "all men are mortal" and the specific instance "Socrates is a man," we can deduce that "Socrates is mortal." Deductive reasoning is commonly used in the field of formal logic and mathematics, and its arguments are either valid (necessarily true) or invalid (fallacious).

In critical thinking, both inductive and deductive reasoning are essential. Inductive reasoning allows us to form hypotheses and theories based on observed data, while deductive reasoning helps us test these hypotheses and draw reliable conclusions. By mastering these reasoning techniques, we can enhance our ability to analyze, construct, and evaluate arguments effectively in both academic and everyday contexts.

"Data!data!data!" he cried impatiently. "I can't make bricks without clay.”

― Sir Arthur Conan Doyle, “The Adventure of the Copper Beeches” - a Sherlock Holmes Short Story pg 4 Version 3.1 under Public Domain

Perhaps you have lost a shoe, a homework assignment, the car keys…a mystery to be solved. How do you solve it? How do you find the lost items? How do you solve the mystery? It is elementary, of course, we use our logic. Specifically, we use inductive and deductive reasoning.

One of the most famous solvers of literary mystery is Sherlock Holmes. Perhaps you have heard of him? Holmes is an excellent example of how to put into use inductive and deductive reasoning.

Of course, we don’t have to be solving a mystery to use inductive and deductive reasoning. These forms of logic are used from government leaders to the everyday citizen. As we explore these forms of reasoning, see if you can recognize when you have or are using them!

This diagram summarizes some of the key terminology related to arguments: they can be either deductive, in which the conclusion follows the general premises, or inductive, in which a probable conclusion is reached based on some observed premises. You can determine the validity or the strength of an argument by assuming that the premises are true, then seeing if the conclusion is the expected result.

Inductive Reasoning

Inducere, the Latin for induction, means to lead in. It is to reason to a conclusion about a class based on carefully looking at a few members of the class. Thus it looks, usually, from the smaller to the larger. When we use inductive reasoning, we observe, find data, draw inferences about patterns or meanings. We use this kind of reasoning when the sample size is so big we can’t look at everything. How many fish are in the sea? We sample and estimate to find this answer. We use enumeration to do this. Enumeration means to count off one by one or determine the number by counting. Induction uses counting to find statistics. Then we use the result to figure out the most likely conclusion based on controlled sampling of pieces.

This jar of M&Ms has exactly 500 candies.

This second jar of M&Ms has exactly 500 candies.

This third jar of M&Ms has exactly 500 candies.

(Thus) all jars of M&Ms must have exactly 500 candies.

Observe that the conclusion says ‘must’, showing that this conclusion is a guess. It is a probability, estimate or extrapolation. A fourth jar might be opened and contain 450 candies, then you will know that sample size is not large enough and thus you are not able to make an accurate conclusion. Then you need to redo the test–count more samples until you find a more reliable sample size to make a more accurate average.

The sun rose two days ago.

The sun rose yesterday.

The sun rose this morning.

Therefore the sun rises every day.

Although this example uses the same number of samples as the previous one, its conclusion is a scientific fact. This is because the actual sample size is much larger. 365 days per year times 4.5 billion years (or even just 300,000 years that humans have been around to observe it) equals a very, very large sample. The size of the sample and the consistency of the result allow us to reach a stronger conclusion. Compare the following two examples? Which of these inductive arguments is strong, and which one is fallacious? Why?

Chickens lays eggs.

Pigeons lay eggs.

Hummingbirds lay eggs.

Kiwis lay eggs.

Therefore all birds lay eggs.

Most universities and colleges in Utah ban alcohol from campus.

Therefore most universities and colleges in the U.S. ban alcohol from campus.

Keep in mind that conclusions drawn from samplings are just estimations and not 100% accurate. However, they can still help us form decisions. For example, if you have a phone that has a battery life starts to get shorter–perhaps it lasts five minutes less for a month, and then the next month it is ten minutes less, and then the next month it is twenty minutes less and so on, then you can induce that the battery will soon not hold a charge. If your phone also has a cracked screen and the microphone is starting to fail, you can decide if it is time to buy a new phone or fix the phone you have. If the problems continue to increase, it is clear that a new phone is the best investment of your money.

Inductive reasoning may also look at analogies to draw conclusions. An analogy is a comparison between two things that seem different. Many teachers use analogies to help students understand unfamiliar subjects by comparing to something the student may be familiar with. In law, analogical reasoning is used when decisions depend on precedent –something similar that has happened before. In science, this kind of reasoning has resulted in discoveries and inventions. For example, Dr. Dennis Stanford, Archeologist for the Smithsonian Institution, in trying to answer the question: when did humans first arrive in North America, observed that Eskimos were able to build boats and navigate the sea to hunt. He used this observation to form an analogy: if Eskimos could do this, could European populations do this as well? By comparing human behavior, he concluded that indeed Early Americans could have arrived by boat from Europe (and Spain) across Atlantic Ice.

Inductive reasoning can also look at patterns , using observation to find details and forms, compare and contrast those to find a pattern. Eventually, finding a pattern can lead to an inference. All the patterns together would be the evidence, which could be called the parts and the generalization of the parts becomes the whole. In medicine, we call this a diagnosis. A doctor will look at the symptoms of a patient to find patterns. The doctor will determine the diagnosis based on analysis of the patterns.

Yet another form of inductive reasoning is looking at cause. Causal reasoning explains why things occur, continue or vanish. Causal reasoning is very important to survival. We use this often without even thinking about it. Causal reasoning influences many of our behaviors. For example, if I eat this chocolate, I may get diabetes. This is because one cause of diabetes can be eating too much sugar. This may be even more important if we have a family history of diabetes. My family has a high risk for diabetes. I am a member of my family; therefore, I have a high risk for diabetes.. This knowledge can help us induce that we need to be more picky about what we eat. It is important to keep in mind that these conclusions need to be accompanied with facts. If there is not evidence, then the conclusion can be fallacious.

We can also reason with hypothesis . Hypothesis means supposition–a tentative explanation for a conclusion. The hypothesis will be tested to find validity or probability. Reasoning through probability, which is about how many times something will occur vs the total number of actual occurrences. Reasoning through statistics , collecting, organizing and interpreting mathematical data, is also used in induction. In statistics, people are working to make accurate predictions from a sampling of parts. It is important to have a well developed sample. If we don’t, we run the risk of committing the fallacy of false statistics.

All of these are how we use inductive reasoning. Keep in mind, inductive reasoning goes from the small to the larger; it is looking out.

Deductive Reasoning

Deductive reasoning is the opposite of inductive reasoning in that it seeks to look in, starting big and moving to smaller. Deducere is the Latin for deduction, which looks at something general to get to the specific. It is known as formal logic because it creates forms that serve as models to show both correct and incorrect reasoning. While induction draws from an accumulation of evidence, deduction reasons by using carefully worded statements about relationships between classes, characteristics and individuals.

In the process of deduction, you begin with some statements, called “premises,” that are assumed to be true, you then determine what else would have to be true if the premises are true. For example, you could begin by assuming that God exists, and is good, and then determine what would logically follow from such an assumption. With this premise, you would look for evidence supporting a belief in God. With deduction, you can provide absolute proof of your conclusions, given that your premises are correct. The premises themselves, however, remain unproven and unprovable.

Let's look at a classic example.

All human beings are mortal (class)

Socrates is human (characteristic)

Socrates is mortal (individual)

This kind of reasoning exercise is called a syllogism. We use syllogisms to help us determine if the argument is valid. An argument has a premise – a claim– that must be supported with facts–or evidence–and this evidence must be analyzed–how is it helping prove the claim. So, we can use a syllogism to test the validity of our argument. First we need to determine if the premises are true and second we determine if the syllogism is valid. If both of these are true, then we have a sound argument. If we grant the premise, we must also grant the conclusion. If we grant the premise and not the conclusion, then we are contradictory, making the argument invalid. A sound argument passes the test of content–premises are true as a matter of fact; it passes the test of form–the premise and conclusion are so related that it is not possible for the premise to be true and the conclusion false. Conversely, an unsound argument does not prove its conclusion and suffers from one or both of two failures–not all premises are true, and the argument is invalid.

Example 6a:

All fathers are males. (class)

Juan is a father. (characteristic)

Juan is male. (individual)

Juan is a member of the class of father, and all members of that class are males, it follows logically that Juan must be a male. But watch what happens when you swap the second two lines.

Example 6b:

Juan is a male. (individual)

The second version is clearly invalid. he error here is that while all fathers are male, not all males are fathers. Just because Juan belongs to the larger class does not necessitate that he participate in the sub-class.

The rule for determining soundness is that if the premises are both true and the argument is valid, the conclusion can’t be false. In order to determine this, we ask: Is the argument vulnerable to criticism on the grounds that one or more of its premises are false? Or are the inferences themselves an issue because even if all premises are true, the conclusion still won’t follow? These are the questions we will consider when determining the validity of an argument.

We can use syllogisms to find if an argument is valid and we can use them to find hidden assumptions.

The burritos at Julio’s are Grandisimos

As a syllogism it reads:

Grandisimo (big) burritos are better (hidden assumption)

Julio's burritos are the biggest

Julio’s Burritos are better (hidden conclusion)

We should buy Julio’s burritos. (Implied conclusion)

(excerpted from Thinking for Yourself 9th edition)

Suspects who are innocent of a crime should be able to have a lawyer present before police questioning. But the thing is you don’t have many suspects who are innocent of a crime…If a person is innocent of a crime, then he is not a suspect.

Here is a translation of this statement into a syllogism:

All innocents are not suspects.

You are a suspect.

You are not innocent.

When you translate the claim, it becomes clear what is wrong with its assertion. The argument is not sound, because the major premise is not true. (341)

Reasoning Together

Inductive and deductive thinking are not wholly separable. They are often intertwined and they are used daily. We can use them to ensure we won’t be killed; we can use them to find our lost keys or solve the world's greatest mysteries! And as you may have come to suspect, compelling arguments rely on using both. Deduction and induction by themselves are inadequate on their own. While deduction gives absolute proof, it never makes contact with the real world, there is no place for observation or experimentation, and no way to test the validity of the premises. And, while induction is driven by observation, it never approaches actual proof of a theory. Therefore an effective paper will include both types of logic.

Figure: Deductive reasoning starts with an understanding of a general principle, then special cases help support that principle. Inductive reasoning works the other way around, where a special case is observed first, which leads to the eventual understanding of a general principle.

Example \(\PageIndex{1}\)

Examples of inductive and deductive reasoning.

Imagine you are a community college student living in a the Central Valley in an agricultural community. You notice that in the past five years, every summer, the water levels in the local reservoir have dropped significantly. Based on these specific observations, you might induce that the region is experiencing a trend of drier summers.

• Observation : The water levels in the local reservoir have dropped significantly every summer for the past five years.
• Pattern Recognition : Summers are becoming drier over time.
• General Conclusion : The Central Valley is experiencing a trend of drier summers.

This inductive reasoning is based on specific data points collected over a period and leads to a broader conclusion about climate trends in the area. However, this conclusion might not be entirely accurate if the observations are not representative of long-term trends or other influencing factors.

Now, consider a scenario where a local farming association publishes a guideline stating, "All farms in the Central Valley that use drip irrigation systems save water compared to those using traditional irrigation methods." You apply this general principle to a specific case on your family's farm.

• General Premise : All farms in the Central Valley that use drip irrigation systems save water.
• Specific Instance : Our farm in the Central Valley uses a drip irrigation system.
• Specific Conclusion : Our farm saves water compared to farms using traditional irrigation methods.

In this deductive reasoning example, the conclusion logically follows from the premises. If the general statement is true and the specific instance fits the criteria, then the conclusion must be true.

Application in Everyday Life

Inductive and deductive reasoning are not just theoretical concepts; they are practical tools used in everyday decision-making. For instance, if you notice that your crops have consistently yielded less during dry years (inductive reasoning), you might decide to invest in drought-resistant crops or advanced irrigation systems. Conversely, if you start with the principle that "drought-resistant crops perform well in dry conditions" (deductive reasoning), you might decide to plant these crops during an expected dry season.

By understanding and applying both inductive and deductive reasoning, you can make more informed and logical decisions, whether you're managing a farm, studying for an exam, or engaging in community debates about resource management. These reasoning skills help you analyze situations, construct valid arguments, and evaluate the claims and evidence presented by others, enhancing your overall critical thinking abilities.

End of Chapter Summary: Inductive vs. Deductive Reasoning, Fallacies, and Argumentative Reading and Writing

Understanding inductive and deductive reasoning, along with the ability to identify fallacies, is crucial for developing strong critical thinking, reading, and writing skills, particularly in the realm of argumentative reading and writing. Deductive reasoning involves starting with a general principle and reaching a specific conclusion, ensuring that if the premises are true, the conclusion must also be true. Inductive reasoning , on the other hand, involves drawing general conclusions from specific observations, which means that while the conclusions are probable, they are not guaranteed.

Recognizing logical fallacies is equally important. Fallacies are errors in reasoning that can undermine the validity of an argument. Common fallacies include ad hominem attacks, straw man arguments, false dilemmas, and appeals to emotion, among others. These fallacies can distort the truth and mislead audiences, which is why critical thinkers and effective writers must be vigilant in identifying and avoiding them.

In today's media landscape, the ability to apply these reasoning skills is more important than ever. Consider the ongoing discussions around climate change. Deductive reasoning might be used to argue that because scientific principles and data show increased greenhouse gases lead to global warming, the current rise in global temperatures is due to human activity. Inductive reasoning can be seen in how scientists gather data from various climate studies to form broader conclusions about climate trends.

However, the debate is often clouded by fallacies. For example, some arguments against climate change use ad hominem attacks on scientists or present false dilemmas, suggesting that we must choose between economic growth and environmental protection. By applying inductive and deductive reasoning and being aware of fallacies, individuals can better navigate these complex discussions, making informed decisions based on sound logic and evidence.

By integrating these concepts into your argumentative reading and writing , you can critically analyze arguments and construct well-reasoned, persuasive essays. Recognizing fallacies in others' arguments allows you to critique their validity effectively, while avoiding these pitfalls in your writing strengthens your position. Using inductive and deductive reasoning enhances your ability to draw and defend logical conclusions.

In summary, mastering inductive and deductive reasoning, coupled with the ability to identify fallacies, equips individuals to analyze arguments critically and construct well-reasoned, persuasive arguments. These skills are invaluable not only in academic settings but also in everyday life, where clear and logical thinking is essential for informed decision-making. By honing these abilities, you will be better prepared to engage in thoughtful, impactful discourse both inside and outside the classroom.

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• Methodology

Inductive Reasoning | Types, Examples, Explanation

Published on 4 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning , where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note: Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

Table of contents

What is inductive reasoning, inductive reasoning in research, types of inductive reasoning, inductive generalisation, statistical generalisation, causal reasoning, sign reasoning, analogical reasoning, inductive vs deductive reasoning, frequently asked questions about inductive reasoning.

Inductive reasoning is a logical approach to making inferences, or conclusions. People often use inductive reasoning informally in everyday situations.

You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion.

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In inductive research, you start by making observations or gathering data. Then, you take a broad view of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

You distribute a survey to pet owners. You ask about the type of animal they have and any behavioural changes they’ve noticed in their pets since they started working from home. These data make up your observations.

To analyse your data, you create a procedure to categorise the survey responses so you can pick up on repeated themes. You notice a pattern : most pets became more needy and clingy or agitated and aggressive.

Inductive reasoning is commonly linked to qualitative research , but both quantitative and qualitative research use a mix of different types of reasoning.

There are many different types of inductive reasoning that people use formally or informally, so we’ll cover just a few in this article:

Inductive reasoning generalisations can vary from weak to strong, depending on the number and quality of observations and arguments used.

Inductive generalisations use observations about a sample to come to a conclusion about the population it came from.

Inductive generalisations are also called induction by enumeration.

• The flamingos here are all pink.
• All flamingos I’ve ever seen are pink.
• All flamingos must be pink.

Inductive generalisations are evaluated using several criteria:

• Large sample: Your sample should be large for a solid set of observations.
• Random sampling : Probability sampling methods let you generalise your findings.
• Variety: Your observations should be externally valid .
• Counterevidence: Any observations that refute yours falsify your generalisation.

Statistical generalisations use specific numbers to make statements about populations, while non-statistical generalisations aren’t as specific.

These generalisations are a subtype of inductive generalisations, and they’re also called statistical syllogisms.

Here’s an example of a statistical generalisation contrasted with a non-statistical generalisation.

Causal reasoning means making cause-and-effect links between different things.

A causal reasoning statement often follows a standard setup:

• You start with a premise about a correlation (two events that co-occur).
• You put forward the specific direction of causality or refute any other direction.
• You conclude with a causal statement about the relationship between two things.
• All of my white clothes turn pink when I put a red cloth in the washing machine with them.
• My white clothes don’t turn pink when I wash them on their own.
• Putting colourful clothes with light colours causes the colours to run and stain the light-coloured clothes.

Good causal inferences meet a couple of criteria:

• Direction: The direction of causality should be clear and unambiguous based on your observations.
• Strength: There’s ideally a strong relationship between the cause and the effect.

Sign reasoning involves making correlational connections between different things.

Using inductive reasoning, you infer a purely correlational relationship where nothing causes the other thing to occur. Instead, one event may act as a ‘sign’ that another event will occur or is currently occurring.

• Every time Punxsutawney Phil casts a shadow on Groundhog Day, winter lasts six more weeks.
• Punxsutawney Phil doesn’t cause winter to be extended six more weeks.
• His shadow is a sign that we’ll have six more weeks of wintery weather.

It’s best to be careful when making correlational links between variables . Build your argument on strong evidence, and eliminate any confounding variables , or you may be on shaky ground.

Analogical reasoning means drawing conclusions about something based on its similarities to another thing. You first link two things together and then conclude that some attribute of one thing must also hold true for the other thing.

Analogical reasoning can be literal (closely similar) or figurative (abstract), but you’ll have a much stronger case when you use a literal comparison.

Analogical reasoning is also called comparison reasoning.

• Humans and laboratory rats are extremely similar biologically, sharing over 90% of their DNA.
• Lab rats show promising results when treated with a new drug for managing Parkinson’s disease.
• Therefore, humans will also show promising results when treated with the drug.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

In deductive reasoning, you make inferences by going from general premises to specific conclusions. You start with a theory, and you might develop a hypothesis that you test empirically. You collect data from many observations and use a statistical test to come to a conclusion about your hypothesis.

Inductive research is usually exploratory in nature, because your generalisations help you develop theories. In contrast, deductive research is generally confirmatory.

Sometimes, both inductive and deductive approaches are combined within a single research study.

Inductive reasoning approach

You begin by using qualitative methods to explore the research topic, taking an inductive reasoning approach. You collect observations by interviewing workers on the subject and analyse the data to spot any patterns. Then, you develop a theory to test in a follow-up study.

Deductive reasoning approach

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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Inductive and Deductive Assignment (McMahon)

The next writing assignment we will be concentrating on will be the construction of persuasive passages using induction, deduction, and expressive language or analogy. These passages should be used to further strengthen and develop your Pro/Con and/or your Rogerian essays.

1. Inductive reasoning is the process of reasoning from specifics to the general. We draw general conclusions based on discrete, specific everyday experiences. Because both writers and readers share this reasoning process, induction can be a highly effective strategy for persuasion. A truly persuasive and effective inductive argument proceeds through an accumulation of many specifics. Within your own essays you should use support from outside sources, personal experience, and specific examples to fully develop your inductive passages. Also, keep in mind that conclusions drawn from inductive reasoning are always only probable. To use induction effectively, a writer must demonstrate that the specifics are compelling and thus justify the conclusion but never claim that the conclusion is guaranteed in all situations. In addition, a writer must keep in mind who his/her audience is and what specifics or evidence will persuade the audience to accept the conclusion. Finally, a writer who is reasoning inductively must be cautious of hasty generalizations in which the specifics are inadequate to justify the conclusions.

2. Deductive reasoning is the process of reasoning from general statements agreed to be true to a certain and logical conclusion. Again, like inductive reasoning, deductive reasoning is a familiar strategy we use in our everyday lives and is a potentially effective persuasive strategy. However, unlike inductive reasoning when the conclusion may be justified but is always only probable, the conclusion reached deductively must be logically certain. Most deductive arguments begin with a general statement that has already been "proven" inductively and is now accepted by most people as true. Today, most deductive general statements involve commonly held values or established scientific fact. A writer who uses deduction to frame an argument must be absolutely certain that the general statement is accepted as true and then must demonstrate the relationship between this general statement and the specific claim, thus proving beyond a doubt the conclusion. An effective deductive argument is one in which your audience accepts the general statement and is then logically compelled by the development of the argument to accept your conclusion.

3. An analogy helps a writer further develop and support an idea he/she is trying to convey to a reader. In an analogy a comparison is drawn between the principle idea and something else a reader is familiar with. Thus, the comparison clarifies the principle idea. Analogies within persuasive writing appeal to either a reader's value system or to a reader's reason and logic. Asking a reader to consider an idea, issue, or problem in the context of something else can both clarify the idea and persuade the reader to accept our interpretation of the idea. Please note: analogies only work when the subjects you are comparing have some similarities. If the things you compare are too dissimilar, your readers will dismiss the analogy and fail to be persuaded of your idea.

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How to write a Deductive Essay

Carla johnson.

• June 14, 2023
• How to Guides

Whether you’re a student or a professional writer, you’ve probably written different kinds of essays, each with its own rules and requirements; a deductive essay is one type of essay you might come across. In a deductive essay, you start with a general statement and then use logic to come to a certain conclusion. It’s a type of essay that requires you to think carefully about the evidence and analyze it carefully.

What You'll Learn

Importance of Writing a Deductive Essay

Students and professionals alike need to know how to write a deductive essay. In academia, deductive essays are often assigned as a way to assess a student’s ability to think logically and critically. Deductive essays can be used to make arguments in the legal, scientific, or business worlds.

Writing a deductive essay is not only a useful skill, but it can also be a lot of fun. It lets you look into complicated ideas and come to conclusions based on facts and logic.

Purpose of the Guide

The goal of this guide is to show you how to write a deductive essay step by step. This guide will help you understand the key parts of a deductive essay and how to structure your argument, whether you’re a student who has been assigned one or a professional who wants to get better.

By the end of this guide, you’ll know what a deductive essay is, how to choose a topic, how to organize your argument, and how to revise your essay for maximum impact.

Understanding the Elements of a Deductive Essay

Before we delve into the process of writing a deductive essay, it’s important to understand the key elements that make up this type of essay.

Premise: A premise is a statement that gives an argument its foundation. It’s a general statement that helps prove a point. In a deductive essay, your argument starts with the premise. It’s important to pick a strong premise that fits your topic and can be backed up with evidence.

Conclusion: A conclusion is what you get at the end of your line of reasoning. It is the conclusion you come to based on the evidence and reasoning you present in your essay . Your argument’s conclusion should make sense based on the evidence you gave in your premises.

Syllogism : A syllogism is a logical argument that comes to a conclusion by combining two premises. In a deductive essay, you will build your case with syllogisms. A syllogism has three parts: a main idea, a secondary idea, and a conclusion. The main premise is a general statement, the minor premise is a specific example that supports the main premise, and the conclusion is the logical result of the two premises.

Choosing a Topic for a Deductive Essay

Choosing a topic for your deductive essay is an important step in the writing process. A good topic will make it easier to develop your argument and will engage your reader. Here are some tips for choosing a topic:

– Choose a topic that interests you: Writing about a topic that you’re passionate about will make the writing process more enjoyable and engaging.

– Choose a topic that is relevant : Your topic should be relevant to your audience. It should be something that they care about or that affects them in some way.

– Choose a topic that is debatable : A deductive essay is an argumentative essay, so you’ll need to choose a topic that has two or more sides. This will make it easier to develop your argument and engage your reader.

Here are 50 deductive essay topics to get you started:

1. The impact of social media on mental health

2. The effectiveness of online learning

3. The impact of climate change on the economy

4. The benefits of meditation

5. The impact of video games on children

6. The importance of a healthy diet

7. The impact of technology on society

8. The benefits of exercise

9. The impact of music on the brain

10. The effectiveness of alternative medicine

11. The impact of immigration on the economy

12. The benefits of reading

13. The impact of artificial intelligence on employment

14. The importance of sleep

15. The impact of globalization on culture

16. The benefits of travel

17. The impact of automation on the workforce

18. The importance of education

19. The impact of social media on relationships

20. The benefits of volunteering

21. The impact of income inequality on society

22. The importance of mental health

23. The impact of social media on politics

24. The benefits of learning a second language

25. The impact of technology on privacy

26. The importance of recycling

27. The impact of social media on self-esteem

28. The benefits of art therapy

29. The impact of automation on the environment

30. The importance of renewable energy

31. The impact of social media on communication

32. The benefits of mindfulness

33. The impact of technology on creativity

34. The importance of exercise for mental health

35. The impact of social media on activism

36. The benefits of therapy

37. The impact of automation on transportation

38. The importance of diversity

39. The impact of social media on body image

40. The benefits of pet therapy

41. The impact of technology on ethics

42. The importance of community service

43. The impact of automation on healthcare

44. The benefits of laughter

45. The impact of social media on education

46. The importance of financial literacy

47. The impact of technology on relationships

48. The benefits of nature therapy

49. The impact of automation on education

50. The importance of self-care.

Research and Gathering of Information

Once you’ve decided on a topic, the next step is to do research and find evidence to back up your argument . Research is an important part of writing, and it will help you build a strong argument for your deductive essay.

Why research is important when writing a deductive essay: Research gives you the proof that backs up your argument. It lets you find reliable sources of information and use them to build your case. Without research, your deductive essay won’t be convincing and won’t be believable. Information sources for research: You can get information for your deductive essay from many different places, such as books, academic journals, online articles, and interviews. It’s important to choose sources that can be trusted.

How to judge sources: When judging sources, think about things like the qualifications of the author, when it was published, and the reputation of the publisher. Avoid sources that are biased or can’t be trusted.

Creating a Deductive Essay Outline

Once you have conducted research and gathered information, the next step is to create an outline for your deductive essay. An outline will help you organize your thoughts and ensure that your argument is logical and coherent.

Importance of creating an outline: Creating an outline will help you stay focused and on track as you write your essay . It will also ensure that your argument is well-structured and easy to follow.

Elements of a deductive essay outline:

I. Introduction

– Hook

– Background information

– Thesis statement

II. Premise 1

– Major premise

– Minor premise

– Conclusion

III. Premise 2

IV. Premise 3 (optional)

V. Counterargument (optional)

– Counterargument

– Rebuttal

VI. Conclusion

– Restate thesis statement

– Summarize main points

– Final thoughts

Examples of a deductive essay outline:

– Hook: The impact of social media on mental health

– Background information: Statistics on social media use and mental health issues

– Thesis statement: Social media use is linked to increased rates of anxiety and depression.

– Major premise: Social media use is associated with increased feelings of isolation and loneliness.

– Minor premise: Studies have found that people who spend more time on social media report higher levels of loneliness and social isolation.

– Conclusion: Therefore, social media use is a contributing factor to mental health issues .

– Major premise: Social media use can lead to negative comparisons and self-esteem issues.

– Minor premise: Studies have found that people who spend more time on social media are more likely to compare themselves to others and experience negative self-esteem.

– Conclusion: Therefore, social media use can contribute to mental health issues.

– Restate thesis statement: Social media use is linked to increased rates of anxiety and depression.

– Summarize main points: Social media use can lead to increased feelings of isolation and loneliness, as well as negative comparisons and self-esteem issues.

– Final thoughts: It’s important to be mindful of our social media use and take steps to protect our mental health .

Writing the Introduction of a Deductive Essay

The introduction of a deductive essay is the first impression that your reader will have of your argument. It’s important to make a strong and clear introduction that captures your reader’s attention and sets the tone for the rest of your essay .

Importance of an introduction: The introduction provides context for your argument and sets the stage for your main points. It should also include your thesis statement , which is the central argument of your essay.

Elements of an introduction:

1. Hook: A compelling sentence or question that captures your reader’s attention.

2. Background Information : Information that provides context for your argument and helps your reader understand the topic.

3. Thesis statement: A clear and concise statement that summarizes your argument and sets the tone for your essay.

Examples of a deductive essay introduction:

1. Hook: Imagine waking up to the sound of birds chirping and the scent of fresh flowers in the air.

Background information: Nature has a healing effect on the mind and body, and spending time in nature can reduce stress and improve overall well-being.

Thesis statement: Therefore, spending time in nature should be a priority for everyone.

2. Hook : In today’s fast-paced world, it’s easy to lose sight of what’s important.

Background information: Many people struggle with finding balance in their lives and prioritizing self-care.

Thesis statement: However, by making self-care a priority, we can improve our overall well-being and productivity.

Developing the Body Paragraphs of a Deductive Essay

The body paragraphs of a deductive essay are where you present your evidence and build your argument. It’s important to make sure that your body paragraphs are well-structured, clearly written, and logically organized.

Importance of body paragraphs: The body paragraphs provide the evidence and reasoning to support your thesis statement. They should be well-organized and clearly written to convince your reader of your argument.

Elements of body paragraphs:

1. Topic sentence: A clear and concise sentence that introduces the main point of the paragraph.

2. Evidence: Evidence that supports your argument and is relevant to your topic.

3. Explanation: An explanation of how the evidence supports your argument and connects to your thesis statement.

4. Transition sentence: A clear and concise sentence that connects the current paragraph to the next.

Examples of a deductive essay body paragraphs:

1. Topic sentence: Spending time in nature reduces stress and improves overall well-being.

Evidence: Studies have found that spending time in nature can lower cortisol levels and reduce stress .

Explanation: This evidence supports the thesis statement that spending time in nature should be a priority for everyone, as it has a positive impact on mental and physical health.

Transition sentence: Furthermore, spending time in nature can also improve cognitive functioning and creativity.

2. Topic sentence: Prioritizing self-care improves overall well-being and productivity.

Evidence: Studies have found that practicing self-care activities, such as exercise and meditation, can reduce stress and improve mental and physical health .

Explanation: This evidence supports the thesis statement that self-care should be a priority for everyone, as it has a positive impact on overall well-being and productivity.

Transition sentence: Additionally, prioritizing self-care can also improve relationships and lead to a more fulfilling life.

Crafting the Conclusion of a Deductive Essay

The conclusion of a deductive essay is your final opportunity to leave a lasting impression on your reader. It’s important to craft a conclusion that summarizes your argument and leaves your reader with a clear understanding of your position.

Importance of a conclusion: The conclusion provides closure to your argument and reinforces your thesis statement . It should leave your reader with a clear understanding of your position and the importance of your argument.

Elements of a conclusion:

1. Restate thesis statement: A clear and concise restatement of your thesis statement.

2. Summary of main points: A brief summary of the main points covered in your essay .

3. Final thoughts : A final thought that reinforces the importance of your argument and encourages your reader to take action.

Examples of a deductive essay conclusion:

1. Restate thesis statement: Spending time in nature should be a priority for everyone.

Summary of main points: In this essay, we have explored the positive effects of spending time in nature on mental and physical health , as well as cognitive functioning and creativity.

Final thoughts: By prioritizing time in nature, we can improve our overall well-being and productivity, and enjoy the many benefits that nature has to offer.

2. Restate thesis statement: Prioritizing self-care improves overall well-being and productivity.

Summary of main points: Throughout this essay, we have examined the many benefits of self-care, including improved mental and physical health, reduced stress, and increased productivity.

Final thoughts: By making self-care a priority, we can improve our quality of life and achieve our goals with greater ease. It’s important to take care of ourselves so that we can be our best selves for ourselves and for those around us.

Editing and Revising a Deductive Essay

Editing and revising are important steps in the writing process that ensure your deductive essay is clear, concise, and effective. By taking the time to edit and revise, you can catch errors and make improvements that will strengthen your argument and engage your reader.

Importance of editing and revising : Editing and revising help to improve the clarity and effectiveness of your writing. By reviewing your work and making edits, you can catch errors and make improvements that will strengthen your argument and engage your reader.

Tips for editing and revising:

1. Take a break: Step away from your essay for a little while before reviewing it. This will help you come back to it with fresh eyes.

2. Read it out loud: Reading your essay out loud can help you catch errors and improve the flow of your writing .

3. Use a checklist: Create a checklist of common errors to look for, such as spelling and grammar mistakes.

4. Get feedback: Ask someone else to read your essay and provide feedback. This can help you identify areas for improvement that you may have missed.

Examples of edited and revised deductive essays:

Before editing:

Spending time in nature is good for you. It has many benefits.

After editing:

Spending time in nature has numerous benefits for both mental and physical health. Studies have shown that spending time in nature can lower cortisol levels, reduce stress, and improve cognitive functioning and creativity. By prioritizing time in nature, we can improve our overall well-being and productivity.

Before revision:

Self-care is important. It helps you feel better.

After revision:

Prioritizing self-care is essential for improving overall well-being. By engaging in activities such as exercise and meditation, we can reduce stress, improve mental and physical health , and increase productivity. By making self-care a priority, we can achieve our goals and live a more fulfilling life.

Frequently Asked Questions about Deductive Essays

1. what is a deductive essay.

A deductive essay is a type of academic essay in which an argument is presented and supported by evidence, reasoning, and logic. The essay begins with a thesis statement that presents the main argument, and the body paragraphs provide evidence and reasoning to support the thesis.

2. What are the elements of a deductive essay?

The elements of a deductive essay include a clear thesis statement, well-organized body paragraphs, and a conclusion that summarizes the main points and reinforces the thesis statement.

3. How do I choose a topic for a deductive essay?

When choosing a topic for a deductive essay, it’s important to choose a topic that is debatable and has sufficient evidence to support your argument. You can choose a topic based on your interests , current events, or a specific question or problem that you want to address.

4. What is the structure of a deductive essay?

The structure of a deductive essay includes an introduction with a clear thesis statement, body paragraphs that provide evidence and reasoning to support the thesis, and a conclusion that summarizes the main points and reinforces the thesis statement.

5. What is a deductive essay thesis?

A deductive essay thesis is a clear and concise statement that presents the main argument of the essay. It should be specific and debatable, and provide a clear direction for the rest of the essay.

6. What are some common mistakes to avoid in writing a deductive essay?

Common mistakes to avoid in writing a deductive essay include using fallacious reasoning, presenting weak or irrelevant evidence, failing to address counterarguments, and lacking a clear and concise thesis statement.

10 Fascinating Deductive Essay Examples

1. The Effects of Social Media on Mental Health

2. The Benefits of a Plant-Based Diet

3. The Importance of Early Childhood Education

4. The Ethics of Genetic Engineering

5. The Impact of Climate Change on Global Health

6. The Relationship Between Technology and Productivity

7. The Role of Government in Providing Healthcare

8. The Effects of Music on the Brain

9. The Importance of Financial Literacy in Education

10. The Ethics of Animal Testing

In conclusion, writing a deductive essay requires careful planning, research, and organization. It’s important to choose a relevant and debatable topic , present clear and concise arguments, use relevant and credible evidence, and address counterarguments. By following these guidelines and learning from deductive essay examples, you can write an effective and compelling essay that engages your reader and makes a strong argument .

Whether you’re a student or a professional writer, you’ve probably written different kinds of essays, each with its own rules and requirements. A deductive essay is one type of essay you might come across.

In a deductive essay, you start with a general statement and then use logic to come to a certain conclusion. It’s a type of essay that requires you to think carefully about the evidence and analyze it carefully.

Conclusion: A conclusion is what you get at the end of your line of reasoning. It is the conclusion you come to based on the evidence and reasoning you present in your essay. Your argument’s conclusion should make sense based on the evidence you gave in your premises.

Once you’ve decided on a topic, the next step is to do research and find evidence to back up your argument. Research is an important part of writing, and it will help you build a strong argument for your deductive essay.

Importance of creating an outline: Creating an outline will help you stay focused and on track as you write your essay. It will also ensure that your argument is well-structured and easy to follow.

– Conclusion: Therefore, social media use is a contributing factor to mental health issues.

– Final thoughts: It’s important to be mindful of our social media use and take steps to protect our mental health.

The introduction of a deductive essay is the first impression that your reader will have of your argument. It’s important to make a strong and clear introduction that captures your reader’s attention and sets the tone for the rest of your essay.

Importance of an introduction: The introduction provides context for your argument and sets the stage for your main points. It should also include your thesis statement, which is the central argument of your essay.

2. Background information: Information that provides context for your argument and helps your reader understand the topic.

Evidence: Studies have found that spending time in nature can lower cortisol levels and reduce stress.

Evidence: Studies have found that practicing self-care activities, such as exercise and meditation, can reduce stress and improve mental and physical health.

Importance of a conclusion: The conclusion provides closure to your argument and reinforces your thesis statement. It should leave your reader with a clear understanding of your position and the importance of your argument.

2. Summary of main points : A brief summary of the main points covered in your essay.

Summary of main points: In this essay, we have explored the positive effects of spending time in nature on mental and physical health, as well as cognitive functioning and creativity.

2. Read it out loud: Reading your essay out loud can help you catch errors and improve the flow of your writing.

Prioritizing self-care is essential for improving overall well-being. By engaging in activities such as exercise and meditation, we can reduce stress, improve mental and physical health, and increase productivity. By making self-care a priority, we can achieve our goals and live a more fulfilling life.

When choosing a topic for a deductive essay, it’s important to choose a topic that is debatable and has sufficient evidence to support your argument. You can choose a topic based on your interests, current events, or a specific question or problem that you want to address.

In conclusion, writing a deductive essay requires careful planning, research, and organization. It’s important to choose a relevant and debatable topic, present clear and concise arguments, use relevant and credible evidence, and address counterarguments. By following these guidelines and learning from deductive essay examples, you can write an effective and compelling essay that engages your reader and makes a strong argument.

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What’s the Difference Between Inductive and Deductive Reasoning?

Let’s say you’re sitting in class, and you make any random claim like “Professor Smith never eats fast food” or “the neighbor’s dog is stealing my dog’s toys.”

Your friend might tell you to “prove it” or might ask, “How do you know?”

What your friend is  actually wondering is what type of reasoning you used to make your claim and what evidence you can present to illustrate the validity of your claim.

The terms inductive and deductive reasoning might be pretty fuzzy in your mind right now. But you’ve probably used one of these types of reasoning to reach conclusions.

So what’s the difference between inductive and deductive reasoning, and how do you distinguish between the two if you’re asked to use one (or both) in a paper?

Here’s what you need to know.

What Is Reasoning?

In essence, reasoning is the process of logically and rationally thinking about something in order to draw a conclusion. That conclusion is based on the specific evidence you evaluate.

Depending on your assignment , you might use evidence from sources , evidence from personal research , or personal opinion and/or observations .

Remember, reasoning means you’re developing conclusions and making inferences. You’re not proving that something is true.

In other words, you can’t say that you’ve proved all gas stations sell pizza because you visited 10 gas stations and all 10 sold pizza by the slice.

Your observations might infer or suggest that all gas stations sell pizza, but they don’t  prove it to be true.

Now that you know the basics, let’s talk about the two key types of reasoning: inductive and deductive reasoning.

Inductive and Deductive Reasoning Defined

Inductive reasoning.

With inductive reasoning , you use facts, patterns, and other information to reach a conclusion. It uses specific examples to create a more generalized theory. It’s sometimes referred to as “bottom-up” logic.

This type of reasoning is more open-ended. It allows you to explore ideas and theories.

For example, let’s say that, during Monday’s lecture, Professor Smith mentions that fast food restaurants serve food that’s too high in sodium and fat. On Wednesday, he states that prices of fast food restaurants may be cheap (in some cases), but the food they serve is barely edible.

Let’s also say that this pattern continues on a somewhat regular basis. Throughout the semester, he makes negative comments about the nutritional value of fast food.

Based on his pattern of statements, you can use inductive reasoning to conclude that Professor Smith doesn’t eat at fast food restaurants.

You’ve used specific examples of your professor’s comments to infer a larger theory or premise about his behavior.

Keep in mind, though, that your conclusion might not always be valid. It’s perfectly acceptable to change your theory and/or reach new conclusions after you examine new evidence.

In this example, even though your professor made numerous negative comments about fast food restaurants, you might learn that he occasionally (or even frequently) eats at them.

Because your original conclusion is invalid, you might examine the evidence again to draw different conclusions.

In this case, you might look at the professor’s background and realize that he’s a nutrition expert. Thus, you might now conclude that he’s informing the class of the nutritional value of fast food meals. He’s not expressing a personal opinion or stating his personal dining preferences.

When to use inductive reasoning in an essay

When you’re using inductive reasoning, you’re presenting a conclusion based on specific facts, examples, or observations. (You don’t already have a theory in mind. You’re using evidence to help you develop that theory.)

You may choose to use inductive reasoning when writing a narrative essay, a work of fiction , or perhaps a persuasive essay .

Using inductive reasoning in these types of essays allows you to present information to keep the audience interested. Including evidence and examples along the way encourages readers to keep reading in order to learn about each part of the story and enjoy the journey as the tale unfolds.

You might also use inductive reasoning in social science classes.

Using inductive reasoning in essays, such as observation essays , allows you to observe patterns in behaviors and draw conclusions based on what you’ve witnessed or based on experiments you’ve conducted.

Inductive reasoning examples

Below are two examples of how you might use inductive reasoning in an observation essay.

Example #1:

You observe students’ dining preferences in the food court throughout the day. You notice that students who pay with a meal card consistently have trays overflowing with food, but students who pay in cash have consistently lesser amounts of food.

Based on the amounts of food students have on their trays and how they pay for their meals, you might use inductive reasoning to conclude that prepaid meal cards encourage students to spend more frivolously (and eat more food).

This may be because their parents have paid for the meal plan or because they swipe a card instead of paying with actual cash.

You might also conclude that students paying cash are using their own money and therefore must be more careful with how much they spend (and how much they can afford to eat).

Example #2:

You place a poster in a lounge in the freshman dorm encouraging students to volunteer at the local animal shelter. You place the same poster in the same area of a senior dorm.

Then you observe the area for several days and notice that more seniors than first-year students stop to read the poster. You also notice that more seniors take a photo of the poster in order to share the volunteer opportunity or to save the contact information.

Based on which group of students viewed and snapped pics of the poster more, you might use inductive reasoning to conclude that seniors are more likely than first-year students to volunteer. Or you might conclude that seniors are more likely than first-year students to be animal lovers.

In both of these examples, you’ve used  inductive reasoning .

You’ve used the results of your observations to draw broader conclusions about student behavior. (You didn’t have a theory about student behavior before you began your observations. Instead, you developed your conclusions based on what you observed.)

Looking for an example of inductive reasoning in an essay? Read An Observation Essay About a Classmate .

Deductive reasoning

Deductive reasoning begins with a theory or hypothesis and then tests it to determine whether the theory is valid. It’s sometimes referred to as “top-down” reasoning.

This type of reasoning is more specific or narrowed as you’re testing or attempting to confirm a theory or hypothesis.

For example, you might begin with the hypothesis that the neighbor’s dog is wandering into your yard and stealing your dog’s favorite toys.

You’ve started with a general theory. Now you need to use deductive reasoning to test your theory to see whether it’s valid.

To test it, you buy a few new toys, let your dog play with them for a while, and then leave them in the yard. You watch the backyard all afternoon but don’t see any sign of the neighbor’s dog.

In the morning, the toys are gone. You revise your theory to state that the neighbor’s dog must be stealing the toys only at night.

To test your theory, you buy more toys, set up cameras, and check the footage in the morning. You realize that there is indeed a thief stealing your dog’s toys, but it’s not the neighbor’s dog. It’s a raccoon.

In this example, you’ve correctly used deductive reasoning but have determined that the original hypothesis is invalid.

Again, reasoning and testing theories don’t always mean you get the “right” answer each time.

When using deductive reasoning , you often have to test a variety of theories in order to reach a valid conclusion. (Ask any scientist. Most scientists will tell you all about deductive reasoning and creating test after test!)

When to use deductive reasoning in an essay

Deductive reasoning begins with a hypothesis , then tests and attempts to support that hypothesis.

If you’re thinking academic writing, a hypothesis of your paper might also be called a thesis statement . The evidence to support the hypothesis (or thesis statement) will be the evidence (like paraphrases , quotes , and summaries ) from your research sources .

Thus, most academic essays—such as  argumentative essays , compare and contrast essay s, and  literary analyses —will use deductive reasoning .

Scientific writing (like lab reports ) will also use deductive reasoning .

Deductive reasoning examples

Below are two examples of how you might use deductive reasoning in an essay.

If your general hypothesis (or thesis) is that video games encourage aggressive behavior, you’ve started with a basic premise and need to provide evidence to support that premise.

You might use evidence from scholarly journals, credible websites , or interviews with experts, and use deductive reasoning to support your claim and develop your core arguments.

If you’re conducting a few culinary experiments, you might start with the premise that replacing water with cola when heating a can of condensed chicken noodle soup will create a mouth-watering culinary delight.

Based on your experiments of adding different types of colas to different brands of soup, you might use deductive reasoning to conclude that your original premise was invalid. Replacing water with cola does not make for a tasty soup.

In both of these examples, you’ve used deductive reasoning  as you’ve started with a basic premise and used evidence (from sources or from your own experiments) to help reach conclusions.

Interested in reading an example of deductive reasoning in an essay? Check out The Importance of the Implementation of World Wide Immunization to Protect Our Children .

A Reason to Celebrate

Learning new stuff is always cause for celebration. But earning an awesome grade on your paper because you now understand the difference between inductive and deductive reasoning is even greater cause for celebration!

If you’re not quite ready to celebrate, though, and are looking for a few more tips and tricks to help you get that “A,” check out this useful essay advice:

• How to Write a Paragraph That Supports Your Thesis
• 12 Examples of Good Topic Sentences (and Why They Work)
• 10 Essay Writing Rules to Throw Out the Window

If you’d rather hold off on your celebrations until a professional editor has taken a look at your paper, send it our way .

Psst... 98% of Kibin users report better grades! Get inspiration from over 500,000 example essays .

About the Author

Susan M. Inez is a professor of English and writing goddess based out of the Northeast. In addition to a BA in English Education, an MA in Composition, and an MS in Education, Susan has 20 years of experience teaching courses on composition, writing in the professions, literature, and more. She also served as co-director of a campus writing center for 2 years.

Inductive VS Deductive Reasoning – The Meaning of Induction and Deduction, with Argument Examples

If you're conducting research on a topic, you'll use various strategies and methods to gather information and come to a conclusion.

Two of those methods are inductive and deductive reasoning.

So what's the difference between inductive and deductive reasoning, when should you use each method, and is one better than the other?

We'll answer those questions and give you some examples of both types of reasoning in this article.

What is Inductive Reasoning?

The method behind inductive reasoning.

When you're using inductive reasoning to conduct research, you're basing your conclusions off your observations. You gather information - from talking to people, reading old newspapers, observing people, animals, or objects in their natural habitat, and so on.

Inductive reasoning helps you take these observations and form them into a theory. So you're starting with some more specific information (what you've seen/heard) and you're using it to form a more general theory about the way things are.

What does the inductive reasoning process look like?

You can think of this process as a reverse funnel – starting with more specifics and getting broader as you reach your conclusions (theory).

Some people like to think of it as a "bottom up" approach (meaning you're starting at the bottom with the info and are going up to the top where the theory forms).

Here's an example of an inductive argument:

Observation (premise): My Welsh Corgis were incredibly stubborn and independent (specific observation of behavior). Observation (premise): My neighbor's Corgis are the same way (another specific observation of behavior). Theory: All Welsh Corgis are incredibly stubborn and independent (general statement about the behavior of Corgis).

As you can see, I'm basing my theory on my observations of the behavior of a number of Corgis. Since I only have a small amount of data, my conclusion or theory will be quite weak.

If I was able to observe the behavior of 1000 Corgis (omg that would be amazing), my conclusion would be stronger – but still not certain. Because what if 10 of them were extremely well-behaved and obedient? Or what if the 1001st Corgi was?

So, as you can see, I can make a general statement about Corgis being stubborn, but I can't say that ALL of them are.

What can you conclude with inductive reasoning?

As I just discussed, one of the main things to know about inductive reasoning is that any conclusions you make from inductive research will not be 100% certain or confirmed.

Let's talk about the language we use to describe inductive arguments and conclusions. You can have a strong argument (if your premise(s) are true, meaning your conclusion is probably true). And that argument becomes cogent if the conclusion ends up being true.

Still, even if the premises of your argument are true, and that means that your conclusion is probably true, or likely true, or true much of the time – it's not certain.

And – weirdly enough – your conclusion can still be false even if all your premises are true (my Corgis were stubborn, my neighbor's corgis were stubborn, perhaps a friend's Corgis and the Queen of England's Corgis were stubborn...but that doesn't guarantee that all Corgis are stubborn).

How to make your inductive arguments stronger

If you want to make sure your inductive arguments are as strong as possible, there are a couple things you can do.

First of all, make sure you have a large data set to work with. The larger your sample size, the stronger (and more certain/conclusive) your results will be. Again, thousands of Corgis are better than four (I mean, always, amiright?).

Second, make sure you're taking a random and representative sample of the population you're studying. So, for example, don't just study Corgi puppies (cute as they may be). Or show Corgis (theoretically they're better trained). You'd want to make sure you looked at Corgis from all walks of life and of all ages.

If you want to dig deeper into inductive reasoning, look into the three different types – generalization, analogy, and causal inference. You can also look into the two main methods of inductive reasoning, enumerative and eliminative. But those things are a bit out of the scope of this beginner's guide. :)

What is Deductive Reasoning?

The method behind deductive reasoning.

In order to use deductive reasoning, you have to have a theory to begin with. So inductive reasoning usually comes before deductive in your research process.

Once you have a theory, you'll want to test it to see if it's valid and your conclusions are sound. You do this by performing experiments and testing your theory, narrowing down your ideas as the results come in. You perform these tests until only valid conclusions remain.

What does the deductive reasoning process look like?

You can think of this as a proper funnel – you start with the broad open top end of the funnel and get more specific and narrower as you conduct your deductive research.

Some people like to think of this as a "top down" approach (meaning you're starting at the top with your theory, and are working your way down to the bottom/specifics). I think it helps to think of this as " reductive " reasoning – you're reducing your theories and hypotheses down into certain conclusions.

Here's an example of a deductive argument:

We'll use a classic example of deductive reasoning here – because I used to study Greek Archaeology, history, and language:

Theory: All men are mortal Premise: Socrates is a man Conclusion: Therefore, Socrates is mortal

As you can see here, we start off with a general theory – that all men are mortal. (This is assuming you don't believe in elves, fairies, and other beings...)

Then we make an observation (develop a premise) about a particular example of our data set (Socrates). That is, we say that he is a man, which we can establish as a fact.

Finally, because Socrates is a man, and based on our theory, we conclude that Socrates is therefore mortal (since all men are mortal, and he's a man).

You'll notice that deductive reasoning relies less on information that could be biased or uncertain. It uses facts to prove the theory you're trying to prove. If any of your facts lead to false premises, then the conclusion is invalid. And you start the process over.

What can you conclude with deductive reasoning?

Deductive reasoning gives you a certain and conclusive answer to your original question or theory. A deductive argument is only valid if the premises are true. And the arguments are sound when the conclusion, following those valid arguments, is true.

To me, this sounds a bit more like the scientific method. You have a theory, test that theory, and then confirm it with conclusive/valid results.

To boil it all down, in deductive reasoning:

"If all premises are true, the terms are clear , and the rules of deductive logic are followed, then the conclusion reached is necessarily true ." ( Source )

So Does Sherlock Holmes Use Inductive or Deductive Reasoning?

Sherlock Holmes is famous for using his deductive reasoning to solve crimes. But really, he mostly uses inductive reasoning. Now that we've gone through what inductive and deductive reasoning are, we can see why this is the case.

Let's say Sherlock Holmes is called in to work a case where a woman was found dead in her bed, under the covers, and appeared to be sleeping peacefully. There are no footprints in the carpet, no obvious forced entry, and no immediately apparent signs of struggle, injury, and so on.

Sherlock observes all this as he looks in, and then enters the room. He walks around the crime scene making observations and taking notes. He might talk to anyone who lives with her, her neighbors, or others who might have information that could help him out.

Then, once he has all the info he needs, he'll come to a conclusion about how the woman died.

That pretty clearly sounds like an inductive reasoning process to me.

Now you might say - what if Sherlock found the "smoking gun" so to speak? Perhaps this makes his arguments and process seem more deductive.

But still, remember how he gets to his conclusions: starting with observations and evidence, processing that evidence to come up with a hypothesis, and then forming a theory (however strong/true-seeming) about what happened.

How to Use Inductive and Deductive Reasoning Together

As you might be able to tell, researchers rarely just use one of these methods in isolation. So it's not that deductive reasoning is better than inductive reasoning, or vice versa – they work best when used in tandem.

Often times, research will begin inductively. The researcher will make their observations, take notes, and come up with a theory that they want to test.

Then, they'll come up with ways to definitively test that theory. They'll perform their tests, sort through the results, and deductively come to a sure conclusion.

So if you ever hear someone say "I deduce that x happened", they better make sure they're working from facts and not just observations. :)

TL;DR: Inductive vs Deductive Reasoning – What are the Main Differences?

Inductive reasoning:.

• Based on observations, conversations, stuff you've read
• Starts with information/evidence and works towards a broader theory
• Arguments can be strong and cogent, but never valid or sound (that is, certain)
• Premises can all be true, but conclusion doesn't have to be true

Deductive reasoning:

• Based on testing a theory, narrowing down the results, and ending with a conclusion
• Starts with a broader theory and works towards certain conclusion
• Arguments can be valid/invalid or sound/unsound, because they're based on facts
• If premises are true, conclusion has to be true

And here's a cool and helpful chart if you're a visual learner:

That's about it!

Now, if you need to conduct some research, you should have a better idea of where to start – and where to go from there.

Just remember that induction is all about observing, hypothesizing, and forming a theory. Deducing is all about taking that (or any) theory, boiling it down, and testing until a certain conclusion(s) is all that remains.

Happy reasoning!

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Deductive vs. Inductive

Deductive reasoning uses given information, premises or accepted general rules to reach a proven conclusion. On the other hand, inductive logic or reasoning involves making generalizations based upon behavior observed in specific cases. Deductive arguments are either valid or invalid. But inductive logic allows for the conclusions to be wrong even if the premises upon which it is based are correct. So inductive arguments are either strong or weak.

Comparison chart

For example: All men are mortal. John is a man. Therefore John is mortal. This is an example of valid deductive reasoning. On the other hand, here's an example of inductive reasoning: Most men are right-handed. John is a man. Therefore, John must be right-handed. The strength of this inductive argument depends upon the percentage of left-handed people in the population. In any case, the conclusion may well end up being invalid because inductive reasoning does not guarantee validity of the conclusions.

What is Deductive Reasoning?

Deductive reasoning (top-down logic) contrasts with inductive reasoning (bottom-up logic), and generally starts with one or more general statements or premises to reach a logical conclusion. If the premises are true, the conclusion must be valid. Deductive resasoning is used by scientists and mathematicians to prove their hypotheses .

Sound or Unsound arguments

With deductive reasoning, arguments may be valid or invalid, sound or unsound. If the logic is correct, i.e. the conclusion flows from the premises, then the arguments are valid. However, valid arguments may be sound or unsound. If the premises used in the valid argument are true, then the argument is sound otherwise it is unsound.

For example,

• All men have ten fingers.
• John is a man.
• Therefore, John has ten fingers.

This argument is logical and valid. However, the premise "All men have ten fingers." is incorrect because some people are born with 11 fingers. Therefore, this is an unsound argument. Note that all invalid arguments are also unsound.

Types of deductive logic

Law of detachment.

A single conditional statement is made, and a hypothesis (P) is stated. The conclusion (Q) is then deduced from the statement and the hypothesis. For example, using the law of detachment in the form of an if-then statement: (1.) If an angle A>90°, then A is an obtuse angle. (2.) A=125°. (3.) Therefore, A is an obtuse angle.

The law of Syllogism

The law of syllogism takes two conditional statements and forms a conclusion by combining the hypothesis of one statement with the conclusion of another. For example, (1.) If the brakes fail, the car will not stop. (2.) If the car does not stop, there will be an accident. (3.) Therefore, If the brakes fail, there will be an accident.

We deduced the final statement by combining the hypothesis of the first statement with the conclusion of the second statement.

What is Inductive Reasoning?

Inductive reasoning, or induction, is reasoning from a specific case or cases and deriving a general rule. This is against the scientific method . It makes generalizations by observing patterns and drawing inferences that may well be incorrect.

Cogent and Uncogent Arguments

Strong arguments are ones where if the premise is true then the conclusion is very likely to be true. Conversely, weak inductive arguments are such that they may be false even if the premises they are based upon are true.

If the argument is strong and the premises it is based upon are true, then it is said to be a cogent argument. If the argument is weak or the premises it flows from are false or unproven, then the argument is said to be uncogent.

For example, here is an example of a strong argument.

• There are 20 cups of ice cream in the freezer.
• 18 of them are vanilla flavored .
• Therefore, all cups of ice cream are vanilla.

If in the previous argument premise #2 was that 2 of the cups are vanilla, then the conclusion that all cups are vanilla would be based upon a weak argument. In either case, all premises are true and the conclusion may be incorrect, but the strength of the argument varies.

Types of Inductive Reasoning

Generalization.

A generalization proceeds from a premise about a sample to a conclusion about the population. For example, (1.) A sample S from population P is chose. Q percentage of the sample S has attribute A. (2.) Therefore, Q percentage of the population P has attribute A.

Statistical Syllogisms

A statistical syllogism proceeds from a generalization to a conclusion about an individual. For example, (1.) A proportion Q of population P has attribute A. (2.) An individual X is a member of P. (3.) Therefore, there is a probability which corresponds to Q that X has an attribute A.

More Examples

Examples of deductive reasoning.

Quadrilateral ABCD has sides AB ll CD (parallel) and sides BC ll AD. Prove that it is a parallelogram. In order to prove this, we have to use the general statements given about the quadrilateral and reach a logical conclusion.

Another example of deductive logic is the following reasoning:

• All labrador retrievers are dogs.
• Some labrador retrievers are pets .
• Therefore, some dogs are pets.

Examples of Inductive Reasoning

If the three consecutive shapes are triangle, square and pentagon which would be the next shape? If the reasoner observes the pattern, she will observe that the number of sides in the shape increase by one and so a generalization of this pattern would lead her to conclude that the next shape in the sequence would be a hexagon.

Applications of Inductive and Deductive Reasoning

• Deduction can also be temporarily used to test an induction by applying it elsewhere.
• A good scientific law is highly generalized like that in Inductive reasoning and may be applied in many situations to explain other phenomena.
• Deductive reasoning is used to deduce many experiments and prove a general rule.

Inductive reasoning is also known as hypothesis construction because any conclusions made are based on current knowledge and predictions. As with deductive arguments, biases can distort the proper application of inductive argument, which prevents the reasoner from forming the most logical conclusion based on the clues.

Availability Heuristic

The availability heuristic causes the reasoner to depend primarily upon information that is readily available. People have a tendency to rely on information that is easily accessible in the world around them. This can introduce bias in inductive reasoning.

Confirmation bias

The confirmation bias is based on the natural tendency to confirm, rather than to deny a current hypothesis. For example, for several centuries it was believed that the sun and planets orbit the earth.

• Inductive and Deductive Instruction (for teachers)
• Introduction to Logic (Univ. of Utah)
• Types of Reasoning

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Deductive, Inductive, and Abductive Reasoning (with Examples)

Understanding different types of arguments is an important skill for philosophy as it enables us to assess the strength of the conclusions drawn. In this blog post, we’ll explore the characteristics of three different types of argument and look at some examples:

• Deductive arguments
• Inductive arguments
• Abductive arguments

Deductive Arguments: The Conclusion is Certainly True

Deductive arguments operate on the principle of logical necessity , aiming to provide conclusions that follow necessarily from the premises.

These arguments seek to establish the truth of specific claims based on the truth of general principles or premises. Deductive reasoning allows for definitive and conclusive outcomes if the premises are true.

In other words, deductive arguments are logically watertight: If the premises are true, it’s logically impossible for the conclusion to be false.

General Format of a Deductive Argument:

• Premise 1: General Principle A is true.
• Premise 2: General Principle B is true.
• Premise 3: General Principle C is true.
• Conclusion: Therefore, Specific Claim X is true.
• Premise 1: All dogs are mammals.
• Premise 2: Rex is a dog.
• Conclusion: Therefore, Rex is a mammal.

In this deductive argument, the conclusion follows necessarily from the premises. If we accept the truth of the general principle that all dogs are mammals (1) and the premise that Rex is a dog (2), we are logically compelled to accept the conclusion that Rex is a mammal (3).

Other examples of deductive argument formats include modus ponens and modus tollens .

Note: A deductively valid argument means the conclusion necessarily follows from the premises and so, if the premises of the argument are true, the conclusion must also be true. However, the premises may be false , in which case the conclusion may be false too. For example:

• Premise 1: If today is Monday, the moon is made of green cheese.
• Premise 2: Today is Monday.
• Conclusion: Therefore, the moon is made of green cheese.

This argument is still deductively valid – the conclusion does follow necessarily from the premises – but the conclusion is false because one or more of the premises are false . For more detail on valid reasoning (including the difference between a valid and sound argument) see this post .

Inductive Arguments: The Conclusion is Probably True

Inductive arguments involve reasoning from specific instances or observations to general conclusions or generalisations.

They aim to make general claims based on limited evidence, seeking to establish patterns, trends, or probabilities. While inductive arguments do not guarantee absolute certainty, they offer insights and probabilistic reasoning.

In other words, inductive arguments are not logically watertight – but they nevertheless provide support for the conclusion .

General Format of an Inductive Argument:

• Premise 1: Observation A is true.
• Premise 2: Observation B is true.
• Premise 3: Observation C is true.
• Conclusion: Therefore, it is likely that Generalisation X is true.
• Premise 1: Every bird I have observed can fly.
• Premise 2: The next bird I encounter will likely be able to fly.
• Premise 3: The bird species documented so far exhibit the ability to fly.
• Conclusion: Therefore, it is probable that all birds can fly.

This example illustrates an inductive argument where the conclusion is based on observed instances and generalises the ability of flight to all birds. While the conclusion is likely to be true, it is possible to encounter a bird species that cannot fly (e.g. an ostrich or a penguin), which weakens the argument’s strength.

Another type of inductive argument is an argument from analogy , where because two things are similar in one way they are likely to be similar in another way. For example, if your friend likes the same music as you, this may suggest they will like the same art as you.

Abductive Arguments: The Conclusion is the Best Explanation

Abductive arguments focus on finding the best or most plausible explanation for a given observation or phenomenon.

They involve reasoning from evidence to a hypothesis or explanation that provides the most likely account of the observed facts. An explanation may be considered more likely or plausible because it fits more neatly with the observed data, for example, or because it is the simplest explanation with the fewest assumptions (a principle known as Ockham’s Razor ).

Like inductive arguments, abductive arguments are not logically watertight. Although a hypothesis may seem to be the best explanation, other explanations are still logically possible.

General Format of an Abductive Argument:

• Observation: There is a certain observation or phenomenon.
• Evidence: Supporting evidence related to the observation.
• Hypothesis: A proposed explanation or claim that best accounts for the evidence.
• Conclusion: Therefore, Claim X is the most plausible explanation.
• Observation: The grass in the garden is wet.
• Evidence: There are water droplets on the leaves, and the ground is damp.
• Hypothesis: It rained last night.
• Conclusion: Therefore, the wet grass is most likely due to rain.

In this abductive argument, the wet grass and the presence of water droplets on the leaves and damp ground are the observed evidence. The hypothesis that it rained provides the best explanation for the observed evidence. However, other explanations, such as sprinklers or a hose, are also possible.

Applied to A Level Philosophy

There are various examples of deductive arguments, inductive arguments, and abductive arguments in A level philosophy .

Examples of deductive arguments in A level philosophy:

• The logical problem of evil
• Ontological arguments (e.g. Anselm’s or Malcolm’s )
• Descartes’ trademark argument

Examples of inductive arguments in A level philosophy:

• The evidential problem of evil
• Hume’s teleological argument
• Mill’s response to the problem of other minds

Examples of abductive arguments in A level philosophy:

• Russell’s argument that the external world is the best hypothesis
• Swinburne’s teleological argument

Identifying whether an argument is deductive, inductive, or abductive is a great way to demonstrate detailed and precise knowledge of philosophy and pick up those AO1 marks .

Further, knowing the difference between these types of arguments can also be useful to help evaluate ( AO2 ) the strengths and weaknesses of the various arguments you consider in the 25 mark essay questions.

• Straightforward explanations of syllabus topics for all 4 modules
• Bullet point summaries at the end of each module
• Exam blueprint for each question type (with example answers)
• Essay 25 mark essay plans for every major topic
• Glossary of key terms

How to Write a Deductive Essay Like Immanuel Kant?

Did you know that Immanuel Kant, an influential 18th-century German philosopher, significantly contributed to how we write a deductive essay today through his groundbreaking work in epistemology and metaphysics? Kant's emphasis on rationalism and the nature of human cognition profoundly impacted the structure and approach to deductive reasoning in academic discourse. 

In his seminal work, the "Critique of Pure Reason," Kant explored the relationship between a priori knowledge and deductive rationale, arguing that certain truths are inherent in the structure of human thought. This perspective had a lasting impact on how philosophers and writers approached deductive essays, encouraging a deeper consideration of the inherent principles guiding logical thought processes. 

In this article, we will use Kant's insights to show you how to compose engaging deductive essays without straining yourself. 

What Is a Deductive Essay

According to the definition, a deductive essay is a form of academic writing that follows a logical and structured approach to presenting an argument or thesis. In this type of essay, the author begins with a general premise or hypothesis and then provides specific evidence and examples to support and validate the initial assertion. The deductive process involves moving from the general to the specific, ultimately leading to a well-founded conclusion.

Unlike inductive reasoning, which derives general principles from specific observations, papers start with a broad statement and work towards a more specific and nuanced understanding. Every essay writer sees the purpose of a deductive essay to convince the reader of the validity of the central claim through a carefully crafted sequence of logical steps and evidence, demonstrating a clear and persuasive line of thought.

Deductive vs Inductive Writing Styles

Deductive and inductive reasoning represent contrasting approaches to logical thinking and are fundamental in shaping the structure of arguments and essays. Deductive writing begins with a general statement or hypothesis and then narrows down to specific conclusions through a series of logical steps. The process is characterized by moving from the broader to the more specific, aiming to demonstrate the inherent truth of the initial proposition. If the general premise in deductive thinking is true and the logical steps are valid, the conclusion is deemed certain. This form of rationale is often associated with formal logic and mathematical proofs, making it a structured and rigorous method for constructing arguments.

On the other hand, the difference between deductive and inductive reasoning starts with specific observations or evidence and moves toward broader generalizations or theories. Unlike deductive logic, inductive arguments do not guarantee the truth of their conclusions. Instead, they aim to establish a likely probability. Inductive writing is prevalent in scientific inquiry, where empirical observations lead to formulating hypotheses and theories. It acknowledges the inherent uncertainty in drawing broad conclusions from specific instances and allows for knowledge development through cumulative evidence and repeated observations. Both deductive and inductive writing styles play vital roles in critical thinking and shaping the persuasive power of various forms of discourse, including essays and academic writing.

Deductive Essay Example

Check out this deductive essay example designed to elucidate the methodology and highlight how deductive reasoning constructs a persuasive argument. Study our analysis of the correlation between smartphone usage and sleep quality to observe the effectiveness of this logical writing approach in practical application. By the way, if you enjoy this example and want a paper of similar quality, try our custom research paper writing solution for a quick and consistent result.

How to Write a Deductive Essay

Writing a deduction essay involves several key aspects contributing to its effectiveness and coherence. By paying attention to these aspects, writers can effectively convey their deductive reasoning, creating essays that are both persuasive and intellectually satisfying.

1. Clear Thesis Statement

Begin with brainstorming deductive essay topics and then presenting a clear and concise thesis statement that conveys the main argument or hypothesis. This statement serves as the foundation for the entire essay and guides the reader in understanding the central claim.

2. Logical Structure

Deductive essays require a well-organized structure that follows a logical progression. Typically, the essay moves from a general premise to specific evidence and then to a conclusive statement. Each paragraph should build upon the previous one, creating a coherent and convincing argument.

3. Evidential Support

Providing strong evidence to support the central thesis is crucial in deductive reasoning. Relevant examples, facts, or data should support each step in the argument. This evidential support enhances the credibility of the essay and strengthens the logical flow of ideas.

4. Clarity in Reasoning

Deduction essays demand clarity in reasoning. Each step in the logical sequence should be explicit and easy for the reader to follow. Avoid ambiguity and ensure that the connections between the general premise, specific evidence, and the conclusion are transparent.

5. Conclusion and Recapitulation

A deductive essay concludes by summarizing the key points and restating the thesis in light of the presented evidence. The conclusion should reaffirm the logical connections established throughout the essay and leave a lasting impression on the reader, reinforcing the validity of the central argument. If you want to learn how to write an essay fast , this guide will definitely help!

What Are Deductive Arguments

Deductive arguments form a category of reasoning where the conclusion logically follows from the premises, providing a form of certainty if the premises are true. These arguments are characterized by moving from the general to the specific, and the structure ensures that if the premises are accurate, the conclusion must be true. In other words, deductive reasoning is concerned with the necessity of the conclusion based on the provided premises. This process mirrors a top-down approach, where a broad statement or hypothesis leads to more specific, grounded outcomes through a series of logical steps.

If you want to really learn how to write deductive essay, presenting a rigid deductive argument is a must-do. If the initial premise is true and the reasoning is valid, the conclusion is considered certain or logically necessary. Deductive arguments are prevalent in mathematics, formal logic, and various scientific disciplines where precision and certainty are essential. Philosophers like Aristotle and later logicians have extensively studied and formalized deductive reasoning, contributing to its prominence in logical discourse.

While deductive arguments offer a high degree of certainty, it is crucial to distinguish them from inductive reasoning. Inductive arguments involve moving from specific observations to broader generalizations and only provide a degree of probability rather than certainty. Deductive reasoning, emphasizing logical necessity, is fundamental in constructing rigorous and convincing arguments in various academic and intellectual domains.

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Structure of a DeducDeductive Essay Structure

Much like a well-orchestrated symphony, a deductive essay unfolds with precision, building from a sweeping general premise to a finely tuned conclusion. This essay structure symbolizes a deliberate journey where each paragraph serves as a stepping stone, leading readers through the intricate maze of deductive reasoning. In the symphony of words, the introduction sets the stage, the body paragraphs harmonize evidence, and the conclusion orchestrates a powerful finale, leaving an indelible imprint of logical prowess. So, let's unravel the layers of how to write an academic essay where persuasion meets the elegance of structured thoughts.

Introduction 

The structure of a deduction essay is characterized by a systematic progression from a general premise to a specific conclusion. The essay typically begins with an introduction with a clear and concise thesis statement, presenting the overarching argument. This thesis serves as the foundation for the subsequent development of the essay. Following the introduction, the body paragraphs unfold logically, each contributing to the overall deductive reasoning.

In the body of the essay, each paragraph is dedicated to a specific aspect or piece of evidence that supports the thesis. The writer starts with a general statement, laying out the initial premise, and then presents detailed evidence or examples. These specifics gradually lead the reader toward a more specific and focused understanding of the central argument. The logical progression ensures that each step in the argument is built upon the previous one, creating a coherent and convincing line of reasoning.

The conclusion of a deductive essay serves to summarize the key points and restate the thesis in light of the evidence provided. It reaffirms the logical connections established throughout the essay and emphasizes the validity of the central argument. The essay structure, therefore, mirrors the process of deductive reasoning itself, guiding the reader through a carefully crafted sequence of logical steps to arrive at a well-founded conclusion. This approach is essential for constructing a persuasive and intellectually satisfying composition. To learn more, consult our guide on how to write a conclusion for an essay .

Deductive Essay Key Considerations

Several key considerations merit thoughtful attention to ensure the effectiveness and persuasiveness of the argument presented. One fundamental aspect is the formulation of a clear and well-defined thesis statement. This statement is the guiding beacon for the entire essay, articulating the central premise from which logical deductions will flow. The clarity in the thesis not only aligns the writer's focus but also provides readers with a roadmap for the forthcoming journey of deductive reasoning.

Equally crucial is the logical structure of the essay. Deductive essays demand a systematic arrangement that moves seamlessly from the general to the specific. Each paragraph should be a carefully calibrated step in the logical sequence, building a persuasive case for the validity of the central argument. The interconnection of ideas and the seamless transition from one point to the next contribute significantly to the overall coherence and impact of the essay.

Moreover, the provision of compelling evidential support cannot be overstated. Deductive reasoning hinges on the strength and relevance of the evidence presented. Writers must meticulously select examples, facts, or data that directly support each logical step, reinforcing the argument's credibility. A well-supported deductive essay not only persuades but also instills confidence in the reader regarding the soundness of the conclusion drawn.

Finally, key writing considerations encompass the formulation of a clear thesis, the establishment of a logical structure, and the incorporation of compelling evidence. By addressing these considerations with precision, writers can construct deductive essays that not only showcase intellectual prowess but also leave a lasting impact on the audience.

Deductive Essay Writing Tips

Writing a deductive essay involves presenting a logical argument based on premises and drawing a conclusion. Remember that writing relies on the strength of the logic and evidence presented. Here are some tips to help you craft an effective paper:

1. Understand the Structure:

• Introduction: Provide a brief overview of the topic and state the thesis or main argument.
• Body Paragraphs: Present your premises separately, providing evidence and supporting details for each.
• Conclusion: Summarize the main points and restate the conclusion based on the premises.

2. Cogitate a  Thesis Statement:

• Clearly state your main argument or thesis in the introduction.
• Make sure your thesis is specific and debatable.
• Consult a deductive essay example for inspiration.

3. Identify Premises:

• Clearly state the premises that lead to your conclusion.
• Each premise should be logically connected to the others.

4. Logical Order:

• Present your premises in a logical order, starting with the most general and progressing to the more specific.
• Ensure a clear and coherent flow between paragraphs.

5. Provide Evidence:

• Support each premise with relevant evidence, examples, or data.
• Use credible sources to strengthen your arguments.

6. Avoid Fallacies:

• Be aware of common logical fallacies and avoid using them in your arguments.
• Common fallacies include hasty generalizations, ad hominem attacks, and faulty causation.
• Study the types of tone in writing .

7. Clarity and Precision:

• Use clear and precise language to convey your ideas.
• Define any terms that may be unclear or have multiple interpretations.

8. Counterarguments:

• Address potential counterarguments to strengthen your position.
• Refute counterarguments with logical reasoning and evidence.

9. Conciseness:

• Be concise in your writing. Avoid unnecessary words or information.
• Stick to the relevant points that directly contribute to your argument.

10. Relevance:

• Ensure that all information presented is relevant to the main argument.
• Remove any unnecessary details or tangential information.

11. Proofread and Edit:

• Carefully proofread your essay for grammar, spelling, and punctuation errors.
• Edit for clarity, coherence, and overall effectiveness.
• Ask others to read your essay and provide constructive feedback.

20 Great Deductive Essay Topics

Should you encounter difficulty in selecting topics to explore, do not worry! We have compiled an outstanding list suitable for diverse assignments, ranging from standard homework tasks to more complex projects. Additionally, we have an extensive list of argumentative essay topics that will definitely ignite your creativity!

• How does higher education impact career opportunities and economic success?
• The Impact of technological advancements on human relationships.
• The link between educational attainment and economic success.
• The relationship between environmental conservation and economic growth: Exploring the sustainability paradigm.
• What role does early childhood education play in long-term academic achievement?
• Can universal basic income lead to increased employment rates and economic stability?
• The influence of social media on mental health: Investigating the connection between online presence and well-being.
• Assessing the strengths and challenges of a multicultural workforce.
• Analyzing the relationship between government policies and income inequality.
• The impact of early childhood education on long-term academic achievement.
• How will artificial intelligence affect employment in the future of work?
• A connection between physical activity and cognitive function.
• The intersection of gender and leadership: Unpacking stereotypes and examining gender disparities in leadership positions.
• Investigating the link between socioeconomic status and health outcomes.
• Analyzing the role of media in shaping public opinion.
• The relationship between immigration and economic growth.
• How parental support impacts educational achievement.
• Analyzing the future of work in the age of automation.
• Investigating the link between social support networks and mental health.
• Does government spending have a positive or negative impact on economic growth?

Deductive essays offer college students valuable opportunities to enhance critical thinking and analytical skills. Through the systematic presentation of premises leading to a logical conclusion, students develop the ability to analyze information, identify patterns, and draw reasoned inferences. Engaging with deductive reasoning encourages students to structure their thoughts methodically, fostering clarity in communication. 

These essays also promote effective problem-solving as students must assess evidence, evaluate its relevance, and construct a compelling argument. Moreover, such essays provide a platform for honing research skills, as students often need to gather and synthesize information to support their claims. In case you’d like to continue improving your skills of convincing readers, we suggest you read our persuasive essay format guide with more interesting information on the topic.

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Daniel Parker

is a seasoned educational writer focusing on scholarship guidance, research papers, and various forms of academic essays including reflective and narrative essays. His expertise also extends to detailed case studies. A scholar with a background in English Literature and Education, Daniel’s work on EssayPro blog aims to support students in achieving academic excellence and securing scholarships. His hobbies include reading classic literature and participating in academic forums.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

“Inductive” vs. “Deductive”: How To Reason Out Their Differences

• What Does Inductive Mean?
• What Does Deductive Mean?
• Inductive Reasoning Vs. Deductive Reasoning

Inductive and deductive are commonly used in the context of logic, reasoning, and science. Scientists use both inductive and deductive reasoning as part of the scientific method . Fictional detectives like Sherlock Holmes are famously associated with methods of deduction (though that’s often not what Holmes actually uses—more on that later). Some writing courses involve inductive and deductive essays.

But what’s the difference between inductive and deductive ? Broadly speaking, the difference involves whether the reasoning moves from the general to the specific or from the specific to the general. In this article, we’ll define each word in simple terms, provide several examples, and even quiz you on whether you can spot the difference.

⚡ Quick summary

Inductive reasoning (also called induction ) involves forming general theories from specific observations. Observing something happen repeatedly and concluding that it will happen again in the same way is an example of inductive reasoning. Deductive reasoning (also called deduction ) involves forming specific conclusions from general premises, as in: everyone in this class is an English major; Jesse is in this class; therefore, Jesse is an English major.

What does inductive mean?

Inductive is used to describe reasoning that involves using specific observations, such as observed patterns, to make a general conclusion. This method is sometimes called induction . Induction starts with a set of premises , based mainly on experience or experimental evidence. It uses those premises to generalize a conclusion .

For example, let’s say you go to a cafe every day for a month, and every day, the same person comes at exactly 11 am and orders a cappuccino. The specific observation is that this person has come to the cafe at the same time and ordered the same thing every day during the period observed. A general conclusion drawn from these premises could be that this person always comes to the cafe at the same time and orders the same thing.

While inductive reasoning can be useful, it’s prone to being flawed. That’s because conclusions drawn using induction go beyond the information contained in the premises. An inductive argument may be highly probable , but even if all the observations are accurate, it can lead to incorrect conclusions.

Follow up this discussion with a look at concurrent vs. consecutive .

In our basic example, there are a number of reasons why it may not be true that the person always comes at the same time and orders the same thing.

Additional observations of the same event happening in the same way increase the probability that the event will happen again in the same way, but you can never be completely certain that it will always continue to happen in the same way.

That’s why a theory reached via inductive reasoning should always be tested to see if it is correct or makes sense.

What else does inductive mean?

Inductive can also be used as a synonym for introductory . It’s also used in a more specific way to describe the scientific processes of electromagnetic and electrostatic induction —or things that function based on them.

What does deductive mean?

Deductive reasoning (also called deduction ) involves starting from a set of general premises and then drawing a specific conclusion that contains no more information than the premises themselves. Deductive reasoning is sometimes called deduction (note that deduction has other meanings in the contexts of mathematics and accounting).

Here’s an example of deductive reasoning: chickens are birds; all birds lay eggs; therefore, chickens lay eggs. Another way to think of it: if something is true of a general class (birds), then it is true of the members of the class (chickens).

Deductive reasoning can go wrong, of course, when you start with incorrect premises. For example, look where this first incorrect statement leads us: all animals that lay eggs are birds; snakes lay eggs; therefore, snakes are birds.

The scientific method can be described as deductive . You first formulate a hypothesis —an educated guess based on general premises (sometimes formed by inductive methods). Then you test the hypothesis with an experiment . Based on the results of the experiment, you can make a specific conclusion as to the accuracy of your hypothesis.

You may have deduced there are related terms to this topic. Start with a look at interpolation vs. extrapolation .

Deductive reasoning is popularly associated with detectives and solving mysteries. Most famously, Sherlock Holmes claimed to be among the world’s foremost practitioners of deduction , using it to solve how crimes had been committed (or impress people by guessing where they had been earlier in the day).

However, despite this association, reasoning that’s referred to as deduction in many stories is actually more like induction or a form of reasoning known as abduction , in which probable but uncertain conclusions are drawn based on known information.

Sherlock’s (and Arthur Conan Doyle ’s) use of the word deduction can instead be interpreted as a way (albeit imprecise) of referring to systematic reasoning in general.

What is the difference between inductive vs. deductive reasoning?

Inductive reasoning involves starting from specific premises and forming a general conclusion, while deductive reasoning involves using general premises to form a specific conclusion.

Conclusions reached via deductive reasoning cannot be incorrect if the premises are true. That’s because the conclusion doesn’t contain information that’s not in the premises. Unlike deductive reasoning, though, a conclusion reached via inductive reasoning goes beyond the information contained within the premises—it’s a generalization , and generalizations aren’t always accurate.

The best way to understand the difference between inductive and deductive reasoning is probably through examples.

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Examples of inductive and deductive reasoning

Examples of inductive reasoning.

Premise: All known fish species in this genus have yellow fins. Conclusion: Any newly discovered species in the genus is likely to have yellow fins.

Premises: This volcano has erupted about every 500 years for the last 1 million years. It last erupted 499 years ago. Conclusion: It will erupt again soon.

Examples of deductive reasoning

Premises: All plants with rainbow berries are poisonous. This plant has rainbow berries. Conclusion: This plant is poisonous.

Premises: I am lactose intolerant. Lactose intolerant people get sick when they consume dairy. This milkshake contains dairy. Conclusion: I will get sick if I drink this milkshake.

Reason your way to the best score by taking our quiz on "inductive" vs. "deductive" reasoning!

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'Deduction' vs. 'Induction' vs. 'Abduction'

What to Know Deductive reasoning, or deduction , is making an inference based on widely accepted facts or premises. If a beverage is defined as "drinkable through a straw," one could use deduction to determine soup to be a beverage. Inductive reasoning, or induction , is making an inference based on an observation, and often an observation of a sample. You can induce that the soup is tasty if you observe all of your friends happily consuming it. Abductive reasoning, or abduction , is making a probable conclusion from what you know. If you see an abandoned bowl of hot soup on the table, you can use abduction to conclude the owner of the soup is likely returning soon.

Do you have to figure out what time you need to leave your house for an appointment? Or are you trying to decide the best choice for lunch? Or are you baffled about why a half-eaten sandwich is on the counter? These situations call for some method of reasoning, and there are three that we use daily: deduction , induction , and abduction .

In abductive reasoning, the major premise is evident, but the minor premise and therefore the conclusion are only probable. For example, if you find a half-eaten sandwich in your home, you might use probability to reason that your teenage son made the sandwich, realized he was late for work, and abandoned it before he could finish it.

Deductive Reasoning

Deduction is generally defined as "the deriving of a conclusion by reasoning." Its specific meaning in logic is " inference in which the conclusion about particulars follows necessarily from general or universal premises ." Simply put, deduction—or the process of deducing —is the formation of a conclusion based on generally accepted statements or facts. It occurs when you are planning out trips, for instance. Say you have a 10 o'clock appointment with the dentist and you know that it takes 30 minutes to drive from your house to the dentist's. From those two facts, you deduce that you will have to leave your house at 9:30, at the latest, to be at the dentist's on time.

Deductive reasoning always follows necessarily from general or universal premises. If a sandwich is defined as "two or more slices of bread or a split roll having a filling in between," and a hot dog is defined as "a frankfurter; especially : a frankfurter heated and served in a long split roll" then one must deduce that any hot dog served in a split roll is a sandwich .

Inductive Reasoning

Whereas in deduction the truth of the conclusion is guaranteed by the truth of the statements or facts considered (the hot dog is served in a split roll and a split roll with a filling in the middle is a sandwich), induction is a method of reasoning involving an element of probability . In logic, induction refers specifically to "inference of a generalized conclusion from particular instances." In other words, it means forming a generalization based on what is known or observed. For example, at lunch you observe 4 of your 6 coworkers ordering the same sandwich. From your observation, you then induce that the sandwich is probably good—and you decide to try it yourself. Induction is at play here since your reasoning is based on an observation of a small group, as opposed to universal premises.

Abductive Reasoning

The third method of reasoning, abduction , is defined as "a syllogism in which the major premise is evident but the minor premise and therefore the conclusion only probable." Basically, it involves forming a conclusion from the information that is known. A familiar example of abduction is a detective's identification of a criminal by piecing together evidence at a crime scene. In an everyday scenario, you may be puzzled by a half-eaten sandwich on the kitchen counter. Abduction will lead you to the best explanation. Your reasoning might be that your teenage son made the sandwich and then saw that he was late for work. In a rush, he put the sandwich on the counter and left.

If you have trouble differentiating deduction , induction , and abduction , thinking about their roots might help. All three words are based on Latin ducere , meaning "to lead." The prefix de- means "from," and deduction derives from generally accepted statements or facts. The prefix in- means "to" or "toward," and induction leads you to a generalization. The prefix ab- means "away," and you take away the best explanation in abduction.

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Frequently asked questions

What’s the difference between inductive and deductive reasoning.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

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

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

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Home — Essay Samples — Religion — Religious Beliefs — Deductive vs. Inductive Arguments: Cosmological and Design

Deductive Vs. Inductive Arguments: Cosmological and Design

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