inductive essay examples

15 Inductive Reasoning Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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inductive reasoning example and definition, explained below

Inductive reasoning involves using patterns from small datasets to come up with broader generalizations. For example, it is used in opinion polling when you poll 1,000 people and use that data to come up with an estimate of broader public opinion.

Typically, inductive reasoning moves from the specific to the general; and can be understood as educated guesses, assumptions and/or hypotheses drawn from specific incidents.

However, it also has its weaknesses. It cannot provide concrete evidence because it always relies extrapolation and probability.

Inductive logic or inductive reasoning is often contrasted with deductive reasoning which is where the general moves to the specific (in other words: what is generally assumed to be true as a broader phenomenon is assumed to hold in a specific case or circumstance).

Advantages and Disadvantages of Inductive Reasoning


When you have a big enough sample set, inductive reasoning can be highly accurate in developing general ideas.	Inductive reasoning can lead to incorrect conclusions, especially when a dataset is too small to be an accurate representation of the whole.
Inductive reasoning enables us to model big phenomena that are impossible to directly measure, such as how many stars there are in the universe.	The more generalized our assumptions become, the less likely they are to be accurate.
Inductive reasoning is used frequently in public policy settings to create targeted interventions for at-risk populations (this is also true of deductive reasoning).	Inductive reasoning leads to stereotyping and about populations that have not been directly examined as case studies (this is also true of deductive reasoning).

Well-Formulated Inductive Reasoning Examples

1. polling and surveys.

“We surveyed 1,000 people across the county and 520 of them said they will vote to re-elect the mayor. We estimate that 52% of the county will vote for the mayor and he will be re-elected.”

Many statisticians make a living from conducting tried-and-true inductive reasoning studies. We often call this “polling data”. Polls will look at a sample size that is often large enough to have a 95% probability of being correct (that is p = <0.05 ) which is the generally accepted threshold of probability in academic studies.

Polls can help governments and politicians to create policies that are responsive to popular opinion.

However, polls are not always right, and often, statisticians have to re-calibrate their metrics after every general election to get a better understanding of polling bias.

For example, if the statisticians conduct their polls by phone, it may be the case that older people tend to answer their phone more than younger people, and older people may skew their vote in one way or another, which skews the overall polling numbers! They need to account for these biases, which makes their job of making generalizations from patterns very difficult at times.

2. Bonus Structure

“In a study of fifteen employees in my business, I found that a 10% bonus structure raised revenues by 20%. I will now roll-out the bonus structure to all employees.”

In this example of reasoning , a business owner has used a small dataset to identify a trend, which gave them sufficient confidence to roll out their intervention across the entire workplace.

If the business owner didn’t do this initial study, they wouldn’t have any indicative data to rely upon in order to feel confident about their decision. Here, we see how inductive reasoning can be used to help us make more informed decisions.

This doesn’t mean that the business owner will have the same success rate when he introduces the bonuses to everyone, but at least he can proceed with greater confidence than before.

3. Seasonal Trends

“For five years in a row, I have seen bears in the woods in June but not May. This year, I expect to wait until June to see a bear in the woods.”

We can also use inductive reasoning to make assumptions in our own lives. In the above example, a person who lives near the woods has identified a seasonal trend that allows them to generalize and predict future patterns.

This sort of seasonal prediction has been around for millennia. Nomads saw patterns in the land and decided to go on annual migrations based on their hypotheses that certain lands would be more fertile at certain times of year. Similarly, agriculturalists use seasonal trends to reason about when to plant their seeds. This doesn’t mean every year will be perfect (to this day, some seasons are terrible for crop yield).

4. Archaeological Digs

“We dug up three pots within a thirty square foot area. We should focus our dig efforts on this area to see what else we can dig up.”

Archaeology also regularly relies upon inductive reasoning. An archaeologist will find signs of human occupation in a location and use those signs as reason the intensify focus on that area.

In these instances, they are inducing that there are likely to be more remnants of civilization around the first remnants due to the assumption that humans may have settled or camped in that specific location.

5. Traffic Patterns

“I have noticed that traffic is bad between 7.30am and 9am. I will drive to the grocery store after 9am to avoid the traffic.”

We even use inductive reason regularly when planning out our days. We make observations about the things around us and use them to make generalizations and predictions.

In the above example, the person has noticed that traffic is worst just before the work day begins, so avoids driving during that period. This is a generalization that can help the person make informed decisions. While it’s not guaranteed that traffic will be better at 9.30am than 8.30am (there may be a car crash at any time of day!), inductive reasoning states that it is likely that traffic will be better at 9.30am than 8.30am.

Poorly-Formulated Inductive Reasoning Examples

6. dog breeds.

“Despite what the government says about Pitt Bulls, the only Pitt Bulls I have ever met were extremely friendly and sweet. Pitt Bulls must therefore not be a dangerous breed.”

While it may well be the case that this person has not personally encountered a hostile or aggressive Pitt Bull, numerous studies have been done indicating that Pitt Bulls, on average, are more aggressive than other dog breeds; whether or not this is inherently true remains speculation. Many cities have also banned the breed since they’ve resulted in the vast majority of dog fatally-related incidents and injuries , relative to the other dog breeds that exist.

This example illustrates how inductive logic goes from specific incidences and applies them as a general rule or conclusion on a given matter.

7. Job Salary and Occupation

“John is a lawyer, and he makes a lot of money. All lawyers make tons of money.”

Appearances can be deceiving, and though basic logic might indicate that something is true, it does not always hold in each situation. While it’s reasonable to assume that people within a certain occupation may earn a lot of money since, generally speaking, the job is associated with a higher salary—it is not always the case in every circumstance.

Some lawyers, for example, do pro-bono work, others may be employed by the government and work as public defenders for individuals that may lack the means to hire their own legal counsel.

8. Nationality

“My dad is Russian and he has blonde hair and blue eyes. All Russian people must have blonde hair and blue eyes.”

This illustrates the inductive reasoning fallacy by moving from an isolated or single case and applying it as a general rule or broadly applicable conclusion. We know that just because a person bears certain physical traits that may be generally affiliated with a geographical region, that does not mean all individuals from the same place will share those same physical traits.

This shows how inductive reasoning can result in incorrect conclusions and/or false assumptions by using specific instances to draw conclusions.

9. Left-Handedness

“All of my siblings are left-handed, and we are all talented artists. People that are left-handed are more creative and artistically inclined than those that are right-handed.”

It could seem reasonable for this person to assume (based on the evidence that they are exposed to,) that left-handed people are naturally more creative and artistic than their right-handed counterparts. Despite appearances, it is not proven that left-handed people are in fact more artistic than right-handed people .

The misstep in logic occurs from making the move from the specific to the general without having sufficient evidence to substantiate the claim as a generally applicable rule.

10. Rainy Weather

“I was in Seattle for a week, and it rained for all seven days I was there. It is always raining in Seattle.”

There’s no question that Seattle gets a lot of rain and is objectively regarded as a very rainy city. Even still, it would be false to conclude that it rains every single day without fail since this is not the case.

To correct the false conclusion or error in logic, we would revise the statement to some form of the following—each day I was in Seattle it rained; therefore, it is often raining in Seattle.

11. Buying Avocados

“While shopping for groceries, I was in the produce section checking for ripe avocados. I picked up one avocado and it was not ripe enough to eat. I picked up another and it was also underripe. There must not be any ripe avocados at this grocery store.”

While it’s possible that there are not any ripe avocados at the grocery store the person is perusing, this is not conclusive until he or she has inspected each avocado in the bin on how its ripeness. It’s clear that picking up a few avocados and determining that they are not ripe enough to eat does not necessarily indicate the remaining avocados in the bin will be underripe. This abrogates logic and demonstrates the error in inductive reasoning.

12. Food Poisoning

“The last time I ate at this Japanese restaurant I got terrible food poisoning. Do not go and eat at this Japanese restaurant because you will get food poisoning and be extremely sick.”

One incident of food poisoning does not indicate a general pattern or broad truth, and it certainly does not follow that just because a person got food poisoning from eating at a restaurant one time, anyone who eats at that same restaurant will necessarily get food poisoning.

The problem with fallacies in inductive reasoning is that it looks to establish a claim on what is true and factual in general, and while it may well be true in an individual case, it is unlikely to hold in each case without fail.

13. Buying A Mattress

“I have purchased four different mattresses on Amazon. None of them were comfortable, and so I returned all four. Amazon doesn’t have good-quality mattresses.”

This takes a similar structure to the previous example on buying avocados. It’s clear how it would be tempting for this person to conclude, based on their personal experience, that Amazon doesn’t have decent mattresses available to purchase.

However, until the person has actually tried each mattress for sale on Amazon, they cannot say conclusively that all mattresses for sale on Amazon are of poor quality. This would be a false assumption that uses the fallacy of inductive reasoning to draw a conclusion.

14. Penguins

“Penguins are birds and they can’t fly. Therefore, it must be true that birds cannot fly.”

Penguins are a kind of bird and cannot fly; but this does not mean that birds, in general, cannot fly. We know birds can fly—so to assume that birds cannot fly because penguins cannot fly is false and uses flawed inductive logic to formulate its conclusion.

If a person saw a crow and said “crows are birds and can fly, so all birds can fly”, it would also be a false inductive generalization. The person should gather a larger dataset of different types of birds before formulating their hypothesis.

15. Rap Music

“The few rap songs that I’ve listened to included remarks that were inappropriate. Therefore, all rap music is inappropriate.”

While rap music can certainly have some uncouth lyrics, it is surely not the case that rap music is inherently bad, or that every single rap song that exists is not acceptable. There are many rap musicians who rap positive lyrics.

Therefore, this is an overgeneralization (often used by parents!) that aims to exclude the good with the bad, rather than taking a more nuanced look at the issue at hand.

Inductive Essays: Tips, Examples, and Topics

Carla johnson.

June 14, 2023
How to Guides

Inductive essays are a common type of academic writing. To come to a conclusion, you have to look at the evidence and figure out what it all means. Inductive essays start with a set of observations or evidence and then move toward a conclusion. Deductive essays start with a thesis statement and then give evidence to support it. This type of essay is often used in the social sciences, humanities, and natural sciences.

The goal of an inductive essay is to look at the evidence and draw a conclusion from it. It requires carefully analyzing and interpreting the evidence and being able to draw logical conclusions from it. Instead of starting with a conclusion in mind and trying to prove it, the goal is to use the evidence to build a case for that conclusion.

You can’t say enough about how important it is to look at evidence before coming to a conclusion. In today’s world, where information is easy to find and often contradictory, it is important to be able to sort through the facts to come to a good decision. It is also important to be able to tell when the evidence isn’t complete or doesn’t prove anything, and to be able to admit when there is uncertainty.

In the sections that follow, we’ll talk about some tips for writing good inductive essays, show you some examples of good inductive essays, and give you some ideas for topics for your next inductive essay. By the end of this article, you’ll know more about how to write an inductive essay well.

What You'll Learn

Elements of an Inductive Essay

Most of the time, an inductive essay has three main parts: an intro, body paragraphs, and a conclusion.

The introduction should explain what the topic is about and show the evidence that will be looked at in the essay . It should also have a thesis statement that sums up the conclusion that will be drawn from the evidence.

In the body paragraphs, you should show and explain the evidence. Each paragraph should focus on one piece of evidence and explain how it supports the thesis statement . The analysis should make sense and be well-supported, and there should be a clear link between the evidence and the conclusion.

In the conclusion, you should sum up the evidence and the conclusion you came to based on it. It should also put the conclusion in a bigger picture by explaining why it’s important and what it means for the topic at hand.

How to Choose a Topic for an Inductive Essay

It can be hard to choose a topic for an inductive essay, but there are a few things you can do that will help.

First, it’s important to look at the assignment prompt carefully. What’s the question you’re supposed to answer? What evidence do you have to back up your claim? To choose a topic that is both possible and interesting , you need to understand the prompt and the evidence you have.

Next, brainstorming can be a good way to come up with ideas. Try writing down all the ideas that come to mind when you think about the prompt. At this point, it doesn’t matter if the ideas are good or not. The goal is to come up with as many ideas as possible.

Once you have a list of possible topics , it’s important to pick one that you can handle and that you’re interested in. Think about how big the topic is and if you will have enough time to analyze the evidence in enough depth for the assignment . Also, think about your own passions and interests. If you choose a topic that really interests you, you are more likely to write a good essay .

Some potential topics for an inductive essay include:

– The impact of social media on mental health

– The effectiveness of alternative medicine for treating chronic pain

– The causes of income inequality in the United States

– The relationship between climate change and extreme weather events

– The effects of video game violence on children

By following these tips for choosing a topic and understanding the elements of an inductive essay, you can master the art of this type of academic writing and produce compelling and persuasive essays that draw on evidence to arrive at sound conclusions.

Inductive Essay Outline

An outline can help you to organize your thoughts and ensure that your essay is well-structured. An inductive essay outline typically includes the following sections:

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay . It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement . The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

– Conclusion: The conclusion should summarize the evidence and the conclusion that was drawn from it. It should also provide a broader context for the conclusion, explaining why it matters and what implications it has for the topic at hand.

Inductive Essay Structure

The structure of an inductive essay is similar to that of other types of academic essays. It typically includes the following elements:

– Thesis statement: The thesis statement should summarize the conclusion that will be drawn from the evidence and provide a clear focus for the essay .

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay. It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement. The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

It is important to note that the body paragraphs can be organized in different ways depending on the nature of the evidence and the argument being made. For example, you may choose to organize the paragraphs by theme or chronologically. Regardless of the organization, each paragraph should be focused and well-supported with evidence.

By following this structure, you can ensure that your inductive essay is well-organized and persuasive, drawing on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, and to draw logical connections between the evidence and the conclusion. With practice, you can master the art of inductive essays and become a skilled academic writer.

Inductive Essay Examples

Examples of successful inductive essays can provide a helpful model for your own writing. Here are some examples of inductive essay topics:

– Example 1: The Link Between Smoking and Lung Cancer: This essay could look at the studies and statistics that have been done on the link between smoking and lung cancer and come to a conclusion about how strong it is.

– Example 2: The Effects of Social Media on Mental Health: This essay could look at the studies and personal experiences that have been done on the effects of social media on mental health to come to a conclusion about the effects of social media on mental health.

– Example 3: The Effects of Climate Change on Agriculture: This essay could look at the studies and expert opinions on the effects of climate change on agriculture to come to a conclusion about how it might affect food production..

– Example 4: The Benefits of a Plant-Based Diet: This essay could look at the available evidence about the benefits of a plant-based diet, using studies and dietary guidelines to come to a conclusion about the health benefits of this type of diet.

– Example 5: The Effects of Parenting Styles on Child Development: This essay could look at the studies and personal experiences that have been done on the effects of parenting styles on child development and come to a conclusion about the best way to raise a child.

Tips for Writing an Effective Inductive Essay

Here are some tips for writing acompelling and effective inductive essay:

1. Presenting evidence in a logical and organized way: It is important to present evidence in a clear and organized way that supports the thesis statement and the conclusion. Use topic sentences and transitions to make the connections between the evidence and the conclusion clear for the reader.

2. Considering alternative viewpoints: When analyzing evidence, it is important to consider alternative viewpoints and opinions. Acknowledge counterarguments and address them in your essay, demonstrating why your conclusion is more compelling.

3. Using strong and credible sources: Use credible sources such as peer-reviewed journal articles , statistics, and expert opinions to support your argument. Avoid relying on unreliable sources or anecdotal evidence.

4. Avoiding fallacies and biases: Be aware of logical fallacies and biases that can undermine the credibility of your argument. Avoid making assumptions or jumping to conclusions without sufficient evidence.

By following these tips, you can write an effective inductive essay that draws on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, consider alternative viewpoints, and use credible sources to support your argument. With practice and dedication, you can master the art of inductive essays and become a skilled academic writer.

Frequently Asked Questions

1. what is an inductive essay.

An inductive essay is an academic writing that starts with a set of observations or evidence and then works towards a conclusion. The essay requires careful analysis and interpretation of evidence, and the ability to draw logical conclusions based on that evidence.

2. What are the elements of an inductive essay?

An inductive essay typically consists of an introduction, body paragraphs, and a conclusion. The introduction provides background information and presents the thesis statement. The body paragraphs present the evidence and analyze it in depth. The conclusion summarizes the evidence and the conclusion drawn from it.

3. How do I choose a topic for an inductive essay?

To choose a topic for an inductive essay, carefully analyze the assignment prompt, brainstorm ideas, narrow down the topic, and select a topic that interests you.

4. What is the difference between an inductive essay and a deductive essay?

An inductive essay starts with evidence and works towards a conclusion, while a deductive essay starts with a thesis statement and provides arguments to support it.

5. How do I structure an inductive essay?

An inductive essay typically follows a structure that includes a thesis statement, introduction, body paragraphs, and conclusion.

Inductive essays are an important type of academic writing that require careful analysis and interpretation of evidence to come to a conclusion. By using the advice in this article, you can become a good inductive essay writer. Remember to carefully look at the evidence, think about other points of view, use reliable sources, and stay away from logical errors and biases. In conclusion , learning how to write inductive essays is important for developing critical thinking skills and making arguments that are compelling and convincing. You can make a valuable contribution to your field of study and to society as a whole by looking at the facts and coming to logical conclusions. With practice and hard work , you can learn to write good inductive essays that will help you in school and in your career.

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How to Write an Inductive Essay

Jennifer spirko.

Induction and deduction are opposite forms of reasoning. Deduction is a type of formal logic in which you can arrive at a conclusion based on the truth of generalization. For instance, if all llamas are mammals, and Edgar is a llama, then you may deduce that Edgar is a mammal. Induction takes the opposite approach, arriving at a conclusion by way of a series of specific observations or premises. If Edgar has a long neck and two-toed hooves, has heavy, woolly fur, and comes from the Andes, you may infer, via induction, that he is a llama.

Explore this article

Start with a Question or Guess
Establish Specific Premises
Make an Inductive Leap
Defend the Conclusion

1 Start with a Question or Guess

In your introduction, pose a question or establish a hypothesis. May Flewellen McMillan, in her book on rhetoric, recommends this approach because it holds readers’ interest. As a researcher, you may begin with a question that you want to solve, but by the time you are writing the essay, you should know the answer. Posing the question is a rhetorical strategy. Having pre-planned your essay, you know that he is a llama, but begin by asking, “What kind of animal is Edgar?” You should already have an idea about Hamlet’s madness, but begin your essay by asking, “Is Hamlet truly mad or just pretending?”

2 Establish Specific Premises

Address the guiding question by building a series of premises. These are specific data points that address your question. The type of premises will depend on subject area. In sociology, for instance, a researcher might conduct case studies and draw the initial hypothesis from these observations, explains Gordon Marshall in the “Dictionary of Sociology.” A literature essay, on the other hand, presents observations about a character or theme. For instance, you should present your observations of Hamlet’s behavior, some of which (like his rash killing of Polonius) seems insane, and some of which (like his clever circumvention of Rosencrantz and Guildenstern’s plot) seems sane.

3 Make an Inductive Leap

Because induction draws a conclusion from a series of separate observations, it deals with probability, not certainty, according to David Naugle, philosophy professor at Dallas Baptist University. Deduction proceeds to a firm conclusion that cannot be disputed as long as the premises are sound. With induction, on the other hand, all the premises can still be true individually but not guarantee the result. Edgar can have a long neck, shaggy fur, divided hooves and come from the Andes and still not be a llama. While that is the likeliest conclusion, there is a chance that Edgar is a vicuña. The conclusion about what sort of animal Edgar is requires what rhetoricians call a “leap” in reasoning. It is a matter of probability, not certainty.

4 Defend the Conclusion

If you believe that your conclusion is supported, as much as possible, by the examples or observations, be explicit about this degree of certainty. Readers will only follow the inductive leap as far as it seems reasonable. You have to provide the reasoning that supports your inductive leap by which you arrived at your conclusion. If you conclude that Hamlet is sane, then spell out why his seemingly insane actions are the product of his craftiness. If you conclude that Hamlet is mad, then spell out how his seemingly sane actions could result from an unstable psyche or how they represent momentary bouts of clarity. You can present outside research to support your conclusions, such as prior studies done on the prevalence of llamas versus vicuñas or psychological analyses of unstable behavior that resembles Hamlet’s.

1 The Shortest Way to the Essay, Rhetorical Strategies; May Flewellen McMillan
2 Hamlet; William Shakespeare
3 Dallas Baptist University: Philosophy 2302, Introduction to Logic; David Naugle

About the Author

Jennifer Spirko has been writing professionally for more than 20 years, starting at "The Knoxville Journal." She has written for "MetroPulse," "Maryville-Alcoa Daily Times" and "Some" monthly. She has taught writing at North Carolina State University and the University of Tennessee. Spirko holds a Master of Arts from the Shakespeare Institute, Stratford-on-Avon, England.

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Inductive vs. Deductive Research Approach | Steps & Examples

Inductive vs. Deductive Research Approach | Steps & Examples

Published on April 18, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory .

In other words, inductive reasoning moves from specific observations to broad generalizations . Deductive reasoning works the other way around.

Both approaches are used in various types of research , and it’s not uncommon to combine them in your work.

Inductive research approach, deductive research approach, combining inductive and deductive research, other interesting articles, frequently asked questions about inductive vs deductive reasoning.

When there is little to no existing literature on a topic, it is common to perform inductive research , because there is no theory to test. The inductive approach consists of three stages:

A low-cost airline flight is delayed
Dogs A and B have fleas
Elephants depend on water to exist
Another 20 flights from low-cost airlines are delayed
All observed dogs have fleas
All observed animals depend on water to exist
Low cost airlines always have delays
All dogs have fleas
All biological life depends on water to exist

Limitations of an inductive approach

A conclusion drawn on the basis of an inductive method can never be fully proven. However, it can be invalidated.

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When conducting deductive research , you always start with a theory. This is usually the result of inductive research. Reasoning deductively means testing these theories. Remember that if there is no theory yet, you cannot conduct deductive research.

The deductive research approach consists of four stages:

If passengers fly with a low cost airline, then they will always experience delays
All pet dogs in my apartment building have fleas
All land mammals depend on water to exist
Collect flight data of low-cost airlines
Test all dogs in the building for fleas
Study all land mammal species to see if they depend on water
5 out of 100 flights of low-cost airlines are not delayed
10 out of 20 dogs didn’t have fleas
All land mammal species depend on water
5 out of 100 flights of low-cost airlines are not delayed = reject hypothesis
10 out of 20 dogs didn’t have fleas = reject hypothesis
All land mammal species depend on water = support hypothesis

Limitations of a deductive approach

The conclusions of deductive reasoning can only be true if all the premises set in the inductive study are true and the terms are clear.

All dogs have fleas (premise)
Benno is a dog (premise)
Benno has fleas (conclusion)

Many scientists conducting a larger research project begin with an inductive study. This helps them develop a relevant research topic and construct a strong working theory. The inductive study is followed up with deductive research to confirm or invalidate the conclusion. This can help you formulate a more structured project, and better mitigate the risk of research bias creeping into your work.

Remember that both inductive and deductive approaches are at risk for research biases, particularly confirmation bias and cognitive bias , so it’s important to be aware while you conduct your research.

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

Chi square goodness of fit test
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Inclusion and exclusion criteria

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

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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.

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.

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.

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 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 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.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Home — Essay Types — Inductive Essay

Inductive Essay Examples

Inductive essay topics.

Inductive essay topics are those that require the writer to analyze specific examples or observations in order to draw broader conclusions or patterns. These topics often require the writer to use reasoning and evidence to support their arguments and draw conclusions about a larger concept or phenomenon.

Common Topics

Analyzing the effects of technology on modern society
Examining patterns in human behavior
Exploring the impact of environmental changes on a specific region

These topics often require the writer to gather and analyze data, make observations, and draw conclusions based on their findings.

Inductive essay topics can be both thought-provoking and challenging, as they require critical thinking and the ability to draw connections between specific examples and broader concepts. These topics can also be a great way for writers to explore new ideas and perspectives while honing their analytical and reasoning skills.

Importance of Writing Inductive Essays

Inductive essays are important because they encourage critical thinking and analysis. By requiring the writer to examine specific examples and draw broader conclusions, these essays help develop the ability to make logical connections and reason through evidence. This type of writing also fosters the development of strong research skills, as it often involves gathering and analyzing data to support arguments. Additionally, inductive essays allow writers to explore complex topics and draw new insights, making them a valuable tool for intellectual growth and exploration. Overall, writing inductive essays is essential for honing analytical skills and gaining a deeper understanding of the world around us.

How to Choose a Good Topic for Inductive Essay

When it comes to writing an inductive essay, choosing a good topic is crucial. An inductive essay is a type of essay where the writer presents a specific observation and then draws conclusions based on that observation. Therefore, the topic should be something that can be observed and analyzed to draw meaningful conclusions.

When choosing a topic for an inductive essay, it's important to select something that is specific and focused. This could be a particular event, phenomenon, or trend that can be observed and analyzed. It's also essential to choose a topic that is interesting and relevant to the intended audience.

Additionally, the topic should be one that allows for multiple perspectives and interpretations. This will provide ample material for analysis and conclusion drawing, making the essay more engaging and thought-provoking.

In conclusion, when choosing a topic for an inductive essay, it's important to select something specific, focused, interesting, relevant, and open to multiple perspectives. By keeping these considerations in mind, you can choose a good topic that will lead to a compelling and insightful inductive essay.

Inductive essays require the writer to analyze specific examples or observations in order to draw broader conclusions or patterns. Here are some inductive essay topics to consider:

Technology and Society

The impact of social media on interpersonal relationships
How technology has changed the way we communicate
The effects of smartphones on attention spans
Analyzing the influence of artificial intelligence on the job market
The implications of virtual reality on education and learning

Human Behavior

Patterns in consumer behavior and purchasing decisions
The psychology of decision-making in stressful situations
Cultural differences in nonverbal communication
The effects of peer pressure on adolescent behavior
Analyzing the motivations behind social media trends

Environmental Changes

The impact of climate change on wildlife migration patterns
How deforestation affects local ecosystems
The consequences of pollution on public health
Analyzing the relationship between urbanization and air quality
The effects of natural disasters on community resilience and recovery

These inductive essay topics cover a range of issues and phenomena, allowing for the writer to analyze specific examples and draw broader conclusions about the world around us.

In summary, inductive essay topics require the writer to analyze specific examples or observations in order to draw broader conclusions or patterns. These topics often involve critical thinking, analysis, and reasoning to draw connections between specific examples and broader concepts. From examining the effects of technology on society to analyzing human behavior and exploring the impact of environmental changes, inductive essay topics provide ample opportunities for critical analysis and insight. By choosing a specific, focused, and relevant topic, writers can create compelling and thought-provoking inductive essays that foster intellectual growth and exploration.

The inductive essay type is a form of argumentative writing that begins with specific observations and examples and then moves towards a more general conclusion or thesis. In other words, it starts with the details and builds up to a larger point or idea. This type of essay is often used in scientific and logical reasoning, where evidence and observations are used to support a broader claim.

📄 Learn About: Self Evaluation Essay Examples 🖋️

The inductive essay type is a powerful tool for building a strong and persuasive argument. By starting with specific observations and examples and using logical reasoning, you can create a compelling and effective essay.

Writing Tips for Inductive Essays

When it comes to writing an inductive essay, it’s important to follow a few key tips to ensure that your essay is effective and persuasive. In an inductive essay, the writer starts with specific observations and then moves to a general conclusion or thesis based on those observations. Here are some tips to keep in mind when writing an inductive essay:

Start with specific details: Begin your essay with specific examples, observations, or evidence that support your topic. These details will serve as the foundation for your general conclusion.
Organize your essay logically: As you move from specific details to a general conclusion, make sure that your essay is organized in a clear and logical manner. This will help your reader follow your line of reasoning and understand your conclusion.
Use strong evidence: The strength of your inductive essay relies on the quality of the evidence you present. Make sure to use reliable sources and credible examples to support your argument.
Consider alternative conclusions: In an inductive essay, it’s important to acknowledge and consider alternative conclusions that may arise from the evidence you present. This will help you strengthen your own conclusion by addressing potential counterarguments.

By following these tips, you can effectively write an inductive essay that is well-structured, well-supported, and convincing to your readers.

How to Structure an Inductive Essay

When it comes to writing an inductive essay, it is important to follow a specific structure in order to effectively present your arguments and evidence. The structure of an inductive essay typically consists of three main parts: the introduction, the body, and the conclusion.

In the introduction , you should provide an overview of the topic and introduce the main argument or hypothesis that you will be exploring in the essay. This is also where you can provide some background information and context to help the reader understand the topic.

The body of the essay is where you will present your evidence and arguments to support your main argument or hypothesis. This is where you will use inductive reasoning to draw conclusions based on the evidence you have gathered. It is important to present your evidence in a logical and organized manner, using examples and data to support your claims.

Finally, in the conclusion , you should summarize the main points of your essay and restate your main argument or hypothesis. You can also discuss any implications or further research that may be needed to fully understand the topic.

By following this structure, you can effectively present your arguments and evidence in an inductive essay, leading to a well-organized and persuasive piece of writing.

Why Use Inductive Essay Examples

Inductive essay examples are useful for providing a clear understanding of how to construct an inductive argument. They can help students and writers learn how to effectively gather evidence, draw conclusions, and present logical reasoning in their essays. By studying examples, individuals can grasp the structure and flow of an inductive essay, as well as gain insight into how to support their claims with relevant evidence.

What They are Useful For

Inductive essay examples are useful for students who are learning about inductive reasoning and argumentation. They can also be helpful for writers who want to improve their persuasive writing skills. Additionally, these examples can serve as a guide for individuals who are unfamiliar with the process of constructing an inductive essay and need a starting point.

How to Do it Correctly

To effectively use inductive essay examples, individuals should carefully analyze the structure, evidence, and reasoning presented in the sample essays. They should pay attention to how the writer gathers specific instances to form a general conclusion and how they present their argument in a logical and persuasive manner. By studying these examples, individuals can learn how to construct their own inductive essays with clarity and coherence. Ultimately, using inductive essay examples can help individuals develop their critical thinking and argumentation skills.

Inductive Essay Writing Checklist

Inductive essay examples often include scientific research papers, case studies, and argumentative essays that rely on evidence to draw conclusions. This checklist provides a framework for organizing and presenting evidence in an inductive essay.

✔️ Gather relevant data and evidence to support your argument. ✔️ Organize your evidence in a logical and coherent manner. ✔️ Use specific examples and case studies to illustrate your points. ✔️ Draw conclusions based on the evidence presented. ✔️ Ensure that your conclusions are supported by the evidence. ✔️ Use clear and concise language to convey your ideas. ✔️ Avoid making sweeping generalizations without sufficient evidence. ✔️ Consider alternative viewpoints and address potential counterarguments. ✔️ Review and revise your essay to ensure that it is well-structured and effectively communicates your ideas. ✔️ Double-check your citations and references to ensure that all sources are properly credited.

📘 Follow Up: Opinion Essay Examples 📚

The inductive essay writing checklist is a valuable tool for ensuring that your essay effectively presents and supports your conclusions with evidence. By following this checklist, you can create a well-structured and persuasive inductive essay.

Conclusion for Inductive Essay Examples

Inductive essay examples serve as valuable tools for students and writers to understand how to construct persuasive arguments based on specific observations and evidence. By studying these examples, individuals can learn how to gather relevant data, draw conclusions, and present logical reasoning in their essays. Additionally, using a checklist can help ensure that the essay is well-structured and effectively communicates the main argument. Ultimately, inductive essay examples can help individuals develop their critical thinking and argumentation skills, leading to well-organized and convincing essays.

The Relationship Between Technology Use and Attention Span

In the digital age, the pervasive use of technology has raised concerns about its impact on attention spans. This essay presents findings from a survey conducted among Generation Z individuals to draw inductive conclusions regarding the complex relationship between technology use and attention span. Technology…

The Influence of Political Leadership on Economic Growth

Political leadership plays a pivotal role in shaping a country’s economic trajectory. This essay conducts a comparative analysis of countries under different political leaders to draw inductive conclusions about the impact of leadership on economies. Through examining real-world examples, we aim to uncover the intricate…

The Influence of Parenting Styles on Child Behavior

Parenting styles play a significant role in shaping a child’s behavior and development. This essay delves into the findings of longitudinal studies, which offer insights into the long-term effects of various parenting styles on children. Through inductive analysis, we aim to identify patterns and trends…

The Impact of Cultural Diversity on Workplace Creativity

Cultural diversity within the workplace is a growing phenomenon, especially in multinational corporations that operate in global markets. This essay undertakes a comparative analysis of multinational corporations to draw inductive conclusions about the relationship between cultural diversity and workplace creativity. Through examining real-world examples, we…

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The Evolution of Fashion Trends

Fashion trends have always played a significant role in society, reflecting cultural, social, and economic changes. This essay delves into the historical evolution of fashion trends, drawing on historical records and analysis to identify recurring patterns and influences. Through inductive reasoning, we aim to uncover…

The Effects of Diet and Nutrition on Academic Performance

This essay presents findings from a study conducted among school children to investigate the potential correlation between dietary habits and academic achievement. Through inductive analysis of the study’s results, we aim to shed light on the complex interplay between diet, nutrition, and academic success. The…

The Effects of Climate Change on Global Weather Patterns

Climate change is an urgent global concern, with far-reaching consequences for our planet. One of the most visible manifestations of climate change is its impact on global weather patterns. In this essay, we will utilize recent climate data to draw inductive conclusions about the changing…

Examining the Link Between Exercise and Longevity

Longevity has always been a subject of fascination, and in recent years, the search for the fountain of youth has led many to explore the role of exercise in extending one’s lifespan. This essay delves into the lives of centenarians, individuals who have reached the…

Case Studies from Low-Income Communities

Income inequality has wide-ranging effects on society, and one of its most critical aspects is its impact on healthcare access. This essay delves into the disparities in healthcare access within low-income communities through the examination of case studies. By drawing inductive conclusions from these cases,…

Generalization Versus Stereotypes In Society

In the article “Understanding Generalizations and Stereotypes” Sally Raskoff states that generalization is just a concept to make sense of the world and surroundings. Generalization is also used to describe stereotyping and differentiate sometimes with other objects or creatures. For example, some say that cats…

What is an Inductive essay type?

An inductive essay presents a conclusion drawn from specific examples or evidence, rather than starting with a generalization or thesis statement.

How to write an Inductive essay?

Start with specific observations or evidence, then analyze these examples to form a conclusion, and finally, present this conclusion as the thesis of the essay.

How to structure an Inductive essay?

Begin with specific examples or evidence, analyze these examples to develop a conclusion, and then present this conclusion as the thesis statement. The body paragraphs should provide evidence and analysis to support the conclusion, and the conclusion should summarize the main points and restate the thesis.

What is the purpose of an Inductive essay?

The purpose of an inductive essay is to present a conclusion based on specific evidence, allowing the reader to see how the conclusion was reached and understand the logic behind it.

How to choose a topic for an Inductive essay?

Choose a topic that can be supported by specific examples or evidence, and one that allows for analysis and a clear conclusion to be drawn. Look for topics with multiple examples or instances that can be analyzed to form a conclusion.

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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

Inductive and Deductive Reasoning

Two Ways of Understanding

We have two basic approaches for how we come to believe something is true.

The first way is that we are exposed to several different examples of a situation, and from those examples, we conclude a general truth. For instance, you visit your local grocery store daily to pick up necessary items. You notice that on Friday, two weeks ago, all the clerks in the store were wearing football jerseys. Again, last Friday, the clerks wore their football jerseys. Today, also a Friday, they’re wearing them again. From just these observations, you can conclude that on all Fridays, these supermarket employees will wear football jerseys to support their local team.

This type of pattern recognition, leading to a conclusion, is known as inductive reasoning .

Knowledge can also move the opposite direction. Say that you read in the news about a tradition in a local grocery store, where employees wore football jerseys on Fridays to support the home team. This time, you’re starting from the overall rule, and you would expect individual evidence to support this rule. Each time you visited the store on a Friday, you would expect the employees to wear jerseys.

Such a case, of starting with the overall statement and then identifying examples that support it, is known as deductive reasoning .

Two boxes: General Principle on left, Special Case on right. An arrow above moves from left to right, labeled deductive reasoning. An arrow below moves from right to left, labeled inductive reasoning.

The Power of Inductive Reasoning

You have been employing inductive reasoning for a very long time. Inductive reasoning is based on your ability to recognize meaningful patterns and connections. By taking into account both examples and your understanding of how the world works, induction allows you to conclude that something is likely to be true. By using induction, you move from specific data to a generalization that tries to capture what the data “mean.”

Imagine that you ate a dish of strawberries and soon afterward your lips swelled. Now imagine that a few weeks later you ate strawberries and soon afterwards your lips again became swollen. The following month, you ate yet another dish of strawberries, and you had the same reaction as formerly. You are aware that swollen lips can be a sign of an allergy to strawberries. Using induction, you conclude that, more likely than not, you are allergic to strawberries.

Data : After I ate strawberries, my lips swelled (1st time).

Data : After I ate strawberries, my lips swelled (2nd time).

Data : After I ate strawberries, my lips swelled (3rd time).

Additional Information : Swollen lips after eating strawberries may be a sign of an allergy.

Conclusion : Likely I am allergic to strawberries.

The results of inductive thinking can be skewed if relevant data are overlooked. In the previous example, inductive reasoning was used to conclude that I am likely allergic to strawberries after suffering multiple instances of my lips swelling. Would I be as confident in my conclusion if I were eating strawberry shortcake on each of those occasions? Is it reasonable to assume that the allergic reaction might be due to another ingredient besides strawberries?

This example illustrates that inductive reasoning must be used with care. When evaluating an inductive argument, consider

the amount of the data,
the quality of the data,
the existence of additional data,
the relevance of necessary additional information, and
the existence of additional possible explanations.

Inductive Reasoning Put to Work

A synopsis of the features, benefits, and drawbacks of inductive reasoning can be found in this video.

The Power of Deductive Reasoning

Deductive reasoning is built on two statements whose logical relationship should lead to a third statement that is an unquestionably correct conclusion, as in the following example.

All raccoons are omnivores. This animal is a raccoon. This animal is an omnivore.

If the first statement is true (All raccoons are omnivores) and the second statement is true (This animal is a raccoon), then the conclusion (This animal is an omnivore) is unavoidable. If a group must have a certain quality, and an individual is a member of that group, then the individual must have that quality.

Going back to the example from the opening of this page, we could frame it this way:

Grocery store employees wear football jerseys on Fridays. Today is Friday. Grocery store employees will be wearing football jerseys today.

Unlike inductive reasoning, deductive reasoning allows for certainty as long as certain rules are followed.

Evaluating the Truth of a Premise

A formal argument may be set up so that, on its face, it looks logical. However, no matter how well-constructed the argument is, the additional information required must be true. Otherwise any inferences based on that additional information will be invalid.

Inductive reasoning can often be hidden inside a deductive argument. That is, a generalization reached through inductive reasoning can be turned around and used as a starting “truth” a deductive argument. For instance,

Most Labrador retrievers are friendly. Kimber is a Labrador retriever. Therefore, Kimber is friendly.

In this case we cannot know for certain that Kimber is a friendly Labrador retriever. The structure of the argument may look logical, but it is based on observations and generalizations rather than indisputable facts.

Methods to Evaluate the Truth of a Premise

One way to test the accuracy of a premise is to apply the same questions asked of inductive arguments. As a recap, you should consider

the relevance of the additional data, and
the existence of additional possible explanations.

Determine whether the starting claim is based upon a sample that is both representative and sufficiently large, and ask yourself whether all relevant factors have been taken into account in the analysis of data that leads to a generalization.

Another way to evaluate a premise is to determine whether its source is credible.

Are the authors identified?
What is their background?
Was the claim something you found on an undocumented website?
Did you find it in a popular publication or a scholarly one?
How complete, how recent, and how relevant were the studies or statistics discussed in the source?

Overview and Recap

A synopsis of the features, benefits, and drawbacks of deductive reasoning can be found in this video.

Revision and Adaptation. Provided by : Lumen Learning. License : CC BY: Attribution
Inductive Reasoning. Authored by : Chuck Creager Jr.. Located at : https://youtu.be/wzEOwleZNnA . License : CC BY: Attribution
Deductive Reasoning. Authored by : Chuck Creager Jr.. Located at : https://youtu.be/oBnKgxcdSyM . License : All Rights Reserved . License Terms : Standard YouTube License
The Logical Structure of Arguments. Provided by : Radford University. Located at : http://lcubbison.pressbooks.com/chapter/core-201-analyzing-arguments/ . Project : Core Curriculum Handbook. License : Public Domain: No Known Copyright
Table of Contents

Instructor Resources (available upon sign-in)

Overview of Instructor Resources
Quiz Survey

Reading: Types of Reading Material

Introduction to Reading
Outcome: Types of Reading Material
Characteristics of Texts, Part 1
Characteristics of Texts, Part 2
Characteristics of Texts, Part 3
Characteristics of Texts, Conclusion
Self Check: Types of Writing

Reading: Reading Strategies

Outcome: Reading Strategies
The Rhetorical Situation
Academic Reading Strategies
Self Check: Reading Strategies

Reading: Specialized Reading Strategies

Outcome: Specialized Reading Strategies
Online Reading Comprehension
How to Read Effectively in Math
How to Read Effectively in the Social Sciences
How to Read Effectively in the Sciences
5 Step Approach for Reading Charts and Graphs
Self Check: Specialized Reading Strategies

Reading: Vocabulary

Outcome: Vocabulary
Strategies to Improve Your Vocabulary
Using Context Clues
The Relationship Between Reading and Vocabulary
Self Check: Vocabulary

Reading: Thesis

Outcome: Thesis
Locating and Evaluating Thesis Statements
The Organizational Statement
Self Check: Thesis

Reading: Supporting Claims

Outcome: Supporting Claims
Types of Support
Supporting Claims
Self Check: Supporting Claims

Reading: Logic and Structure

Outcome: Logic and Structure
Rhetorical Modes
Diagramming and Evaluating Arguments
Logical Fallacies
Evaluating Appeals to Ethos, Logos, and Pathos
Self Check: Logic and Structure

Reading: Summary Skills

Outcome: Summary Skills
How to Annotate
Paraphrasing
Quote Bombs
Summary Writing
Self Check: Summary Skills
Conclusion to Reading

Writing Process: Topic Selection

Introduction to Writing Process
Outcome: Topic Selection
Starting a Paper
Choosing and Developing Topics
Back to the Future of Topics
Developing Your Topic
Self Check: Topic Selection

Writing Process: Prewriting

Outcome: Prewriting
Prewriting Strategies for Diverse Learners
Rhetorical Context
Working Thesis Statements
Self Check: Prewriting

Writing Process: Finding Evidence

Outcome: Finding Evidence
Using Personal Examples
Performing Background Research
Listening to Sources, Talking to Sources
Self Check: Finding Evidence

Writing Process: Organizing

Outcome: Organizing
Moving Beyond the Five-Paragraph Theme
Introduction to Argument
The Three-Story Thesis
Organically Structured Arguments
Logic and Structure
The Perfect Paragraph
Introductions and Conclusions
Self Check: Organizing

Writing Process: Drafting

Outcome: Drafting
From Outlining to Drafting
Flash Drafts
Self Check: Drafting

Writing Process: Revising

Outcome: Revising
Seeking Input from Others
Responding to Input from Others
The Art of Re-Seeing
Higher Order Concerns
Self Check: Revising

Writing Process: Proofreading

Outcome: Proofreading
Lower Order Concerns
Proofreading Advice
"Correctness" in Writing
The Importance of Spelling
Punctuation Concerns
Self Check: Proofreading
Conclusion to Writing Process

Research Process: Finding Sources

Introduction to Research Process
Outcome: Finding Sources
The Research Process
Finding Sources
What are Scholarly Articles?
Finding Scholarly Articles and Using Databases
Database Searching
Advanced Search Strategies
Preliminary Research Strategies
Reading and Using Scholarly Sources
Self Check: Finding Sources

Research Process: Source Analysis

Outcome: Source Analysis
Evaluating Sources
CRAAP Analysis
Evaluating Websites
Synthesizing Sources
Self Check: Source Analysis

Research Process: Writing Ethically

Outcome: Writing Ethically
Academic Integrity
Defining Plagiarism
Avoiding Plagiarism
Using Sources in Your Writing
Self Check: Writing Ethically

Research Process: MLA Documentation

Introduction to MLA Documentation
Outcome: MLA Documentation
MLA Document Formatting
MLA Works Cited
Creating MLA Citations
MLA In-Text Citations
Self Check: MLA Documentation
Conclusion to Research Process

Grammar: Nouns and Pronouns

Introduction to Grammar
Outcome: Nouns and Pronouns
Pronoun Cases and Types
Pronoun Antecedents
Try It: Nouns and Pronouns
Self Check: Nouns and Pronouns

Grammar: Verbs

Outcome: Verbs
Verb Tenses and Agreement
Non-Finite Verbs
Complex Verb Tenses
Try It: Verbs
Self Check: Verbs

Grammar: Other Parts of Speech

Outcome: Other Parts of Speech
Comparing Adjectives and Adverbs
Adjectives and Adverbs
Conjunctions
Prepositions
Try It: Other Parts of Speech
Self Check: Other Parts of Speech

Grammar: Punctuation

Outcome: Punctuation
End Punctuation
Hyphens and Dashes
Apostrophes and Quotation Marks
Brackets, Parentheses, and Ellipses
Semicolons and Colons
Try It: Punctuation
Self Check: Punctuation

Grammar: Sentence Structure

Outcome: Sentence Structure
Parts of a Sentence
Common Sentence Structures
Run-on Sentences
Sentence Fragments
Parallel Structure
Try It: Sentence Structure
Self Check: Sentence Structure

Grammar: Voice

Outcome: Voice
Active and Passive Voice
Using the Passive Voice
Conclusion to Grammar
Try It: Voice
Self Check: Voice

Success Skills

Introduction to Success Skills
Habits for Success
Critical Thinking
Time Management
Writing in College
Computer-Based Writing
Conclusion to Success Skills

Inductive Reasoning (Definition + Examples)

When faced with stress, how do you determine the best way to alleviate it? Do you go for a run? Eat something? Listen to loud music? Your choices are often influenced by recalling which solutions have consistently provided relief.

For many people, deciding how to deal with emotions or what they should do next requires looking back to past experiences. If a quick walk around the block made you feel better after a stressful day, you may decide to take a quick walk around the block the next time you have a stressful day. This decision-making process, concluding previous experiences, is known as inductive reasoning .

What is Inductive Reasoning?

Inductive reasoning is a method where specific observations or experiences are used to reach a broader, general conclusion. In contrast to deductive reasoning, which starts with a general statement and examines the possibilities to reach a specific conclusion, inductive reasoning begins with specific examples and tries to form a general rule.

Consider the example I presented earlier:

Premise A: Your mother told you that walking around the block would be good for stress relief.

Premise B: Your doctor recommends 30 minutes of walking daily to relieve stress.

Premise C: Last week, after a stressful day, you walked around the block and felt better.

From these observations, you might conclude that 30 minutes of walking around the block will help alleviate stress.

In essence, inductive reasoning uses individual observations and experiences to formulate more general conclusions.

For inductive reasoning to work, you must collect past experiences and observations. We do this naturally, just by living! Then, when you encounter a situation similar to these past experiences, you draw on them for a better picture of outcomes.

For example, maybe you have dealt with little siblings or cousins who respond to certain reinforcements or punishments in certain ways. When you get a job as a teacher, you may draw on those experiences when you make decisions about discipline because you have concluded that all children respond well to one strategy or another.

Who is the Father of Inductive Reasoning?

Francis Bacon is considered the father of inductive reasoning, as he is considered the father of empiricism. Empiricism is the theory that all of our knowledge is pulled from our experiences and senses (as opposed to more innate knowledge.) Inductive reasoning uses our senses and experiences to make judgments.

Characteristics of Inductive Reasoning

If you understand deductive reasoning , you might notice that the induction process isn’t as solid.

Rather than using broad generalizations, induction takes single experiences or facts as premises. This premise could be something you’ve personally experienced or witnessed or an experience told to you by a friend, parent, TV personality, etc.

The premises provide some evidence to build a conclusion. You might think, “But the conclusion might not be true if you’re just pulling from a one-time occurrence or a handful of experiences.” To that, I say you are right.

Conclusions derived from induction don’t make them the truth. They show the probability of an event occurring. Sure, you are likely to feel better after you’ve walked around the block, but there is no solid guarantee that you will feel less stressed after your next walk.

There are problems within induction (I’ll speak to those later,) but often, our inductive reasoning helps us make the best choices for ourselves. If we find ourselves taking a walk and still feeling stressed, we may use inductive reasoning to break down what other events could affect our stress, our walk, and the connection between the two.

Of course, the statement that “inductive reasoning generally gives us a usable conclusion” is a conclusion derived from inductive reasoning itself.

Exploring the Philosophies Behind Inductive Reasoning

The concept of inductive reasoning has been deeply entwined with philosophical discourse since its inception. Philosophers have contemplated and debated the nature, validity, and limitations of induction. Delving into the philosophical underpinnings of inductive reasoning can offer a richer understanding of the topic.

David Hume and the Problem of Induction: The Scottish philosopher David Hume is perhaps the most famous critic of inductive reasoning. In the 18th century, he posed the "Problem of Induction," which questions the logical justification for making predictions based on past experiences. He argued that we cannot rationally justify the belief that the future will resemble the past, making all inductive conclusions inherently uncertain.
Karl Popper and Falsifiability: 20th-century philosopher Karl Popper proposed a significant shift in scientific methodology, emphasizing falsifiability over verification. Rather than using induction to confirm hypotheses, he believed science should aim to falsify them. For Popper, no number of positive outcomes can confirm a scientific theory, but a single counterexample can disprove it.
Bayesianism: Rooted in the work of the 18th-century statistician and minister Thomas Bayes, Bayesianism is a philosophical approach to probability and induction. Bayesianism focuses on how subjective beliefs should evolve in the light of new evidence. It uses a mathematical framework to incorporate prior beliefs with new evidence to arrive at posterior beliefs.
Pragmatism and Induction: American philosophers like C.S. Peirce argued for the practical value of inductive reasoning. From a pragmatist perspective, even if induction can't be justified in absolute logical terms, it is a necessary and effective tool for navigating our world. Its consistent success in a myriad of contexts, for pragmatists, grants it legitimacy.
Evolutionary Justifications: Some contemporary thinkers suggest that our inductive capacities have evolutionary roots. If our ancestors hadn't been adept at making reliable predictions based on past experiences (e.g., predicting that certain predators are dangerous because of past encounters), they wouldn't have survived. Thus, while induction might never achieve logical certainty, its evolutionary origins could indicate its general reliability.

In essence, while inductive reasoning remains a practical tool for navigating our experiences and predicting outcomes, its philosophical underpinnings reveal a world of debate about its validity, scope, and limitations. Understanding these philosophical perspectives enhances our grasp of the nuanced nature of induction and how it shapes and is shaped by broader epistemological concerns.

Where Is Inductive Reasoning Used?

Pretty much everywhere! Any time we draw on prior experiences to conclude, we can use inductive reasoning. We don’t always do this, but we can!

Examples of Inductive Reasoning In Everyday Life

Inductive reasoning is extremely common in our everyday world. A lot of the decisions you make are based on inductive reasoning. You have a headache and take a painkiller because past experiences have shown you that those painkillers work well in treating headaches.

Maybe you take a certain set of side streets because, in past experiences, it has been faster than the highway.

Maybe you buy CBD oil for your dog because, in the past, he has always responded well to it and not gotten sick.

This is just one type of induction. There are also different types of inductive reasoning that we use every day.

Example 1: If I Leave For Work at ___, I Can Avoid Traffic

Inductive reasoning pulls from our experiences to make conclusions. Let’s say you get a new job and have to be there at 9 a.m. every day. Unfortunately, you tend to encounter a lot of rush-hour traffic on your route. You experiment with leaving for work at different times.

One day, you leave for work at 8:30 and arrive at 9:15. The next day, you leave for work at 8:00 and arrive to work at 8:35. Another day, you leave for work at 8:15 and arrive to work at 8:50.

You conclude that rush hour traffic picks up between 8:15 and 8:30. If you leave for work around 8:15, you’ll get to work on time.

Example 2: You Pet a Cat on Its Head, and It Starts Purring. Pet The Cat On Its Belly and It Hisses…

Inductive reasoning can be used to conclude about one specific person, place, or thing. Let’s say you get a new cat. Your friend tells you that their cat loves belly rubs and asks if your cat is the same way. To answer your friend, you have to draw from past experiences. One time, you pet the cat on its head, and the cat started purring. Petting the cat on its back evoked a similar reaction. But the cat got finicky if you tried to rub its belly. You conclude that your cat doesn’t love belly rubs.

Example 3: Throwing Up From Tequila

Experimentation can lead to a lot of inductive reasoning. Take the first time you got drunk. You probably drank too much and got sick. The next time you got drunk, you also got sick. Later, you drink again - guess what happened? You got sick.

Through your observations and experimentation, you conclude that too much alcohol makes you sick. Sure, someone may have told you that you would get sick after drinking. But if you come to that conclusion through a series of observations and events, you have used inductive reasoning.

Example 4: Geometry

You have used inductive reasoning in your everyday life for years without knowing it. Many people don’t learn about inductive reasoning until they take a psychology course. Others learn about inductive reasoning in geometry or higher-level math classes.

Mathematicians use a specific process to create theorems or proven statements. Inductive reasoning cannot produce fool-proof theorems, but it can start the process. If someone is observing something, for example, that two triangles look congruent, they are using inductive reasoning. They have more work to do before they can prove once and for all that the two triangles are congruent, but inductive reasoning helps them kick things off.

Drawing Conclusions About the Past

Inductive reasoning doesn’t just predict what will happen in the future. It can also tell us what probably happened in the past. Take this example.

Premise A says that most babies where you come from are born in modern hospitals.

Premise B says that your friend Denise was born sometime in the last 20-30 years.

Premise C says that Denise was probably born in a hospital.

Of course, that is likely to be true, but it doesn’t mean it is. If Denise tells you that she was born in the back of a pickup truck, you shouldn’t argue with her based on the conclusion you came to through induction.

So there are many different ways that you can use inductive reasoning to sort out what has happened in the past and what may happen in the future. But you may not always be right.

You can also use analogy to conclude different properties of items. Analogies are comparisons between two things that help to clarify information. Through induction and analogy, you can predict likely characteristics, uses, etc., of different things.

Here’s an example.

Spinach is a green vegetable. It’s high in Vitamin A and Vitamin C but has a slightly bitter taste.

Spinach is a great vegetable to add to a healthy smoothie.

Kale is also a green vegetable. It’s also high in vitamins A and C, with a slightly bitter taste.

Using induction, one may assume that kale is also a great vegetable to add to a healthy smoothie.

We often use this type of induction to replace items that we cannot find at the home or the grocery store.

If I told you that 90% of people like my videos, I probably use inductive reasoning. If you hear any statistic that covers a large population, it was probably derived from inductive reasoning.

Statistics usually come from surveys or studies. We can’t study everyone worldwide, so we divide our studies into small, controlled groups. Once we get data from that control group, we use it to predict how a current population feels, reacts to a drug, makes a decision, etc.

You may take a survey among college students and find out that 66% of the students in the study don’t like cheese. You may conclude that 66% of college students don’t like cheese. You may also use this data to predict whether the next college student you meet will or won’t like cheese.

Control groups may give us a good look at the larger population if professionals choose the participants. They are also only effective at reaching conclusions if the control group is sizable enough.

If you surveyed two women and one of them said that they were a feminist and the other one said they were not, you should not conclude that half of all women are feminists. You would need to expand your survey greatly, account for demographics, and look at your study before you could come to any conclusions worth sharing or publishing.

Test your inductive reasoning skills with this puzzle posted on Reddit !

Problems with Inductive Reasoning

You might have been able to spot some of the holes that we can poke in inductive reasoning.

Small control groups

Poor control groups are one way to come to flawed conclusions through inductive reasoning. The feminist example that I just used happens all the time.

Online surveys conducted by organizations who are not professionals may take a survey from a few hundred people, share the conclusion, and spark outrage from people who may not agree or be offended by the result. But the outrage is all based on lies and false conclusions.

Outside Factors

Arguably the biggest problem with inductive reasoning is that the conclusion is not a guaranteed truth. Sure, you might like spinach in your smoothies, and it is very similar to kale. But you might not like the taste of kale. Or the texture of kale. Or the smoothie you try and make with kale doesn’t blend well with the smoothie you made with spinach.

Does that completely dismantle the entire idea of inductive reasoning? Not at all. But it’s something to be aware of.

Outside factors will almost always impact your conclusions. There are a lot of premises that are not necessarily true that you could use to argue that kale is or is not good in a smoothie based on what you know about the similarities between spinach and kale.

And, just because the first smoothie you make with kale doesn’t taste as good as the smoothie you made with spinach doesn’t mean the next smoothie you make with kale will taste bad. But you might avoid kale for a while based on your past experiences using it in a smoothie. That’s still using inductive reasoning.

Inductive reasoning is easy to do, convenient for finding answers, and works most of the time. But it should not be the sole process used to make conclusions about a group of people, a very important outcome, and other things that may make a huge impact.

Inductive vs Deductive Reasoning

Inductive reasoning is often taught side by side with deductive reasoning. We learn inductive reasoning much earlier than we learn deductive reasoning. Jean Piaget , the famed psychologist in child development , theorized that children develop inductive reasoning around 7.

It makes sense. A child touches a hot stove, and they burn their hand. The next time they are near the hot stove, they are likely to remember what happened the last time they touched the stove. They’ll avoid the hot stove and avoid the burn.

Deductive reasoning comes to children at ages 11 or 12. It’s a form of “top-down” logic to inductive’s “bottom-up” logic.

You need to understand basic, broad facts about the world to conclude them. With deductive reasoning, you don’t have memories of experiences to guide your reasoning. You have to know things like “all dogs are mammals” or “all humans are mortal” to narrow your reasoning down to conclusions that you might not be able to grasp.

Another big difference between induction and deduction is that deductive reasoning has stricter rules. Every premise has to be true. If there are exceptions to the premise, you can’t come to a true conclusion. Your conclusion may be valid, but it won’t be true. (And yes, there is a difference in philosophy.)

Test Your Knowledge of Inductive Reasoning

Alright, it’s test time! I will give you three questions to test your knowledge of induction. No cheating!

First question:

Induction uses ______ as evidence to come to conclusions.

Answer: Past experiences!

Second question:

Can you get the truth from induction?

Answer: Not in the philosophical sense. Since each premise does not have to be true, induction will only tell you what is likely true.

Last question:

Which form of reasoning do children develop first?

A: Deduction

B: Induction

C: They develop them at the same time!

The answer is B. Children develop the ability to learn through inductive reasoning at age 6 or 7. They typically understand—deduction later in their development.

Deductive Reasoning (Definition + Examples)
Circular Reasoning (29 Examples + How to Avoid)
Perceptual Reasoning (Definition + Examples)
Equivocation Fallacy (26 Examples + Description)
Begging the Question Fallacy (29 Examples + Definition)

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Deductive and Inductive Reasoning Essay

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Inductive and Deductive Reasoning: Essay Introduction

Deductive approach, inductive approach, inductive vs deductive: essay conclusion, reference list.

There are different types of reasoning, most of which are explained in psychology books and articles. This paper discusses two types of reasoning – deductive and inductive reasoning using cognitive research. The inductive and deductive reasoning essay you read focuses on teaching science and technical courses in High Schools. It explores cases of science and mathematical teaching in schools.

Deductive reasoning is a logical process where conclusions are made from general cases. General cases are studied, after which conclusions are made as they apply to a certain case (Byrne, Evans and Newstead, 2019). In the context of this deductive reasoning essay, an argument from analogy is one of the examples under deductive reasoning. The rule underlying this module is that in the case where P and Q are similar and have properties a, b, and c, object P has an extra property, “x.” Therefore, Q will automatically have the same extra property, “x,” as the two are similar (Dew Jr and Foreman, 2020).

Most high school students in the United States do come across the argument from the analogy model of deductive reasoning while studying science subjects. Nonetheless, most students do not realize the applicability of this rule. They apply the rule unconsciously. Therefore, high school students should learn about this model of reasoning. This will help them know certain instances under which they should apply this rule when making arguments in science subjects (National Academies of Sciences, Engineering, and Medicine, 2019).

Researches conducted on analogies give a clear way of explaining why student reports have added ideas. While studying scientific subjects, students do make productive analogies. They apply scientific principles, for instance, energy conservation principles, to different settings.

Unproductive analogies are also made by students, for example, in experiments between temperature and heat. Research that compares different forms of analogies gained from visual and animated representations. Such studies distinguish the functions of different brain parts. It emphasizes the benefits of activating correct pathways for specific learning forms. Research on analogies emphasizes on the selection and inclusion of right analogies in the reports. It also encourages the analysis of different analogies (Vygotsky, 2020).

Argument from analogy is one of the tools that students can use to advance reasonable arguments in different science subjects. This is according to a study that was conducted to ascertain the model that can be used by high school students in when solving problems in genetics. Different questions and student-teacher engagements were used to reach the conclusion (Choden and Kijkuakul, 2020).

The major problems in the teaching of science subjects are the lapses in communication. More often, students and teachers in science classrooms rarely share similar purpose on either the subject or the activity. At times, teachers and students assign different meanings to the same concept. This happens in cases where the two have different levels of understanding about the science concepts because most of these concepts are technical (Choden and Kijkuakul, 2020).

In order to improve the understanding of science subjects, students are required to use different approaches. For students to use analogy, they must have an understanding of the concept in question first. The concept is the most important thing as arguments derived from the subject will be concrete when the concept is well grasped.

More models should be used by science teachers in the science classes. The real nature of the models or analogs used for teaching are better understood when they are realistic. Analogs are forms of human interventions in learning. They should be used carefully as poor use may result in mal understanding of the real meaning. Analogs have an aspect of practicality which leaves images in the minds of students.

When used well, a constructive learning environment will be attained. Analogies should be used in a way that students can easily capture or map. Students should also be given room to make suggestions of improving the analogies used by their teachers. Imperfect analogies expose difficulties that arise in describing and explaining scientific ideas that are mostly of an abstract nature (Newton, 2022).

According to Oaksford and Chater (2020), inductive reasoning entails taking certain examples and using the examples to develop a general principle. It cannot be utilized in proving a concept. In inductive reasoning, solutions to problems can be reached even when the person offering the solution does not have general knowledge about the world.

An example of deductive reasoning is the case of ‘Rex the dog’. In this case, a child can make a deduction that is logical when Rex barks even at times when barking itself is an unfamiliar activity. If the child was told that Rex is a cat and that all cats bark, the child would respond with a “yes” when asked whether Rex barks. This is even when Rex does not bark. Under this reasoning, logical deductions are counterfactual in that they are not made in line with the beliefs of the real world (Pellegrino and Glaser, 2021).

On the other hand, inductive reasoning is one of the oldest learning models. Inductive reasoning develops with time as students grow. However, this reasoning has not been fully utilized in schools. It carries many cognitive skills within it. Inductive thinking is used in creative arts in high schools. In creative art subjects, students are expected to build on their learned ideas. The knowledge learned is applied in different contexts. This is the real goal of inductive reasoning (Csapó, 2020).

For the purposes of the inductive reasoning essay, research has revealed that deductive reasoning can be applied in two performance contexts. This includes the school knowledge application and the applicable knowledge context. School knowledge is the knowledge that is acquired at school. This knowledge is mostly applied in situations that are related to schoolwork.

It is applied in a similar context in which it was acquired. This knowledge or reasoning is what the students apply in handling assignments, tests, and examinations in school. It is used to grade students and determine student careers in schools. Applicable knowledge can be easily applied in situations that differ from the context in which the command was acquired (Csapó, 2020).

Research conducted in the United States revealed that the skills students acquire at the elementary level are insufficient. Elementary mathematics teaching lacks a conceptual explanation to the students. When these students get to high school, they need a basis upon which they can understand mathematical formulas and measurements. Therefore, teachers are forced to introduce these students to a higher level of thinking.

The tasks in high school mathematics that require deep thinking are also called high cognitive demand tasks. At this level of thinking, students can understand complex mathematical concepts and apply them correctly. Thus, students are introduced to inductive reasoning (Brahier, 2020).

Students will mostly have a tough time at the introductory to inductive reasoning. Students will get a grasp of concepts, mostly mathematical ones. However, it will take longer for students to develop application skills. Mathematical concepts will be understood by students within a short span.

However, applying the concepts to solve different mathematical problems is another problem. Just like for the two types of knowledge, it has always been hard for students from high school to apply the school concept in the real world. Students acquire the inside, but in most cases, they reserve it for schoolwork only.

When students do not get good tutoring, gaining the transition required to achieve the real concepts becomes difficult. This idea further destroys them and may even cause a total failure to understand and apply inductive reasoning (Van Vo and Csapó, 2022).

The transition from elementary school to high school includes psychological changes. These changes need to be molded by introducing the student to detailed thinking. This gradual process begins with slowly ushering the students to simple concepts. This simple concept builds slowly, and complexity is introduced gradually.

The students’ minds grow as they get used to the hard concepts. Later, the students become more creative and critical in thinking and understanding concepts (Hayes et al., 2019).

Inductive and deductive reasoning are two types of reasoning that borrow from one another. The use of logical conclusion applies in both of them. They are very useful, especially in teaching mathematics and science courses.

Brahier, D. (2020) Teaching secondary and middle school mathematics . Abingdon: Routledge.

Byrne, R.M., Evans, J.S.B. and Newstead, S.E. (2019) Human reasoning: the psychology of deduction . London: Psychology Press.

Choden, T. and Kijkuakul, S. (2020) ‘Blending problem based learning with scientific argumentation to enhance students’ understanding of basic genetics’, International Journal of Instruction , 13(1), pp. 445-462.

Csapó, B. (2020) ‘Development of inductive reasoning in students across school grade levels’, Thinking Skills and Creativity , 37, pp. 1-15.

Dew Jr, J.K. and Foreman, M.W. (2020) How do we know?: an introduction to epistemology . Westmont: InterVarsity Press.

Hayes, B.K. et al. (2019) ‘The diversity effect in inductive reasoning depends on sampling assumptions’, Psychonomic Bulletin & Review , 26, pp.1043-1050.

National Academies of Sciences, Engineering, and Medicine (2019) Science and engineering for grades 6-12: investigation and design at the center . Washington, D.C.: National Academies Press.

Newton, D.P. (2022) A practical guide to teaching science in the secondary school . Milton Park: Taylor & Francis.

Oaksford, M. and Chater, N. (2020) ‘New paradigms in the psychology of reasoning’, Annual Review of Psychology , 71, pp. 305-330.

Pellegrino, J.W. and Glaser, R. (2021) ‘Components of inductive reasoning’, In Aptitude, learning, and instruction (pp. 177-218). Abingdon: Routledge.

Upmeier zu Belzen, A., Engelschalt, P. and Krüger, D. (2021) ‘Modeling as scientific reasoning – the role of abductive reasoning for modeling competence’, Education Sciences , 11(9), pp. 1-11.

Van Vo, D. and Csapó, B. (2022) ‘Exploring students’ science motivation across grade levels and the role of inductive reasoning in science motivation’, European Journal of Psychology of Education , 37(3), pp. 807-829.

Vygotsky, L.S. (2020) Educational psychology . Boca Raton: CRC Press.

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Inductive vs Deductive Research Approach (with Examples)

Inductive vs Deductive Reasoning | Difference & Examples

Published on 4 May 2022 by Raimo Streefkerk . Revised on 10 October 2022.

The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory .

Inductive reasoning moves from specific observations to broad generalisations , and deductive reasoning the other way around.

Both approaches are used in various types of research , and it’s not uncommon to combine them in one large study.

Inductive research approach, deductive research approach, combining inductive and deductive research, frequently asked questions about inductive vs deductive reasoning.

When there is little to no existing literature on a topic, it is common to perform inductive research because there is no theory to test. The inductive approach consists of three stages:

A low-cost airline flight is delayed
Dogs A and B have fleas
Elephants depend on water to exist
Another 20 flights from low-cost airlines are delayed
All observed dogs have fleas
All observed animals depend on water to exist
Low-cost airlines always have delays
All dogs have fleas
All biological life depends on water to exist

Limitations of an inductive approach

A conclusion drawn on the basis of an inductive method can never be proven, but it can be invalidated.

Example You observe 1,000 flights from low-cost airlines. All of them experience a delay, which is in line with your theory. However, you can never prove that flight 1,001 will also be delayed. Still, the larger your dataset, the more reliable the conclusion.

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When conducting deductive research , you always start with a theory (the result of inductive research). Reasoning deductively means testing these theories. If there is no theory yet, you cannot conduct deductive research.

The deductive research approach consists of four stages:

If passengers fly with a low-cost airline, then they will always experience delays
All pet dogs in my apartment building have fleas
All land mammals depend on water to exist
Collect flight data of low-cost airlines
Test all dogs in the building for fleas
Study all land mammal species to see if they depend on water
5 out of 100 flights of low-cost airlines are not delayed
10 out of 20 dogs didn’t have fleas
All land mammal species depend on water
5 out of 100 flights of low-cost airlines are not delayed = reject hypothesis
10 out of 20 dogs didn’t have fleas = reject hypothesis
All land mammal species depend on water = support hypothesis

Limitations of a deductive approach

The conclusions of deductive reasoning can only be true if all the premises set in the inductive study are true and the terms are clear.

All dogs have fleas (premise)
Benno is a dog (premise)
Benno has fleas (conclusion)

Many scientists conducting a larger research project begin with an inductive study (developing a theory). The inductive study is followed up with deductive research to confirm or invalidate the conclusion.

In the examples above, the conclusion (theory) of the inductive study is also used as a starting point for the deductive study.

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.

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

Deductive reasoning is also called deductive logic.

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Part Six: Evaluating Inductive Logic

Chapter Fourteen: Inductive Generalization

There is nothing in which an untrained mind shows itself more hopelessly incapable, than in drawing the proper conclusions from its own experience. —John Stuart Mill, Inaugural Address at St. Andrews

There’s nothing like instances to grow hair on a bald-headed argument. —Mark Twain

Correct Form for Inductive Generalization

The Total Evidence Condition (1): Sample Size
The Total Evidence Condition (2): Random Selection
Evaluating the Truth of Premises about Sampling
Complex Arguments

A certain raja, according to a story told by the Buddha, took all the blind men of Savatthi to show them an elephant. As each one felt the elephant, the raja said, “Tell me, what sort of thing is an elephant?” Those who had been presented with the head answered, “Sire, an elephant is like a pot.” Those who had felt the ear replied, “An elephant is like a winnowing basket.” Those who had been presented with a tusk said it was a plowshare. Those who knew only the trunk said it was a plow; others said the body was a granary; the foot, a pillar; the back, a mortar; the tail, a pestle; the tuft of the tail, a brush. Then they began to quarrel, shouting, “Yes it is!” “No, it is not!” “An elephant is not that!” “Yes, it’s like that!” and so on, until they came to blows over the matter.

In one important way we are all like the blind men examining the elephant: there is much that we wish to understand but do not directly experience. Whether we are tasting a spoonful of soup to see if the pot has enough salt or reading about the polling of registered voters to learn who the electorate prefers for president, we habitually draw general conclusions from a few observations—that is, we habitually reason by inductive generalization.

Many writers actually mean inductive generalization when they write about induction—which helps explain why some have dubiously defined induction itself as inference that moves from the particular to the general. This particular-to-general feature highlights the most fundamental difference between inductive generalization and frequency arguments; frequency arguments, recall, move from the general to the particular.

14.1 Correct Form for Inductive Generalization

If an inductive generalization is to be logically successful, it—like all other inductive arguments—must satisfy both the correct form condition and the total evidence condition. This is typically the correct form for inductive generalizations: [1]

n of sampled F are G (where n is any frequency, including 0 and 1).
∴ n (+ or – m ) of F are G.

Both the premise and the conclusion are frequency statements of the sort described in the preceding chapter. Note that in this form of argument, the premise and conclusion differ in only two ways— sampled is in the premise but not in the conclusion, and the margin of error (+ or – m ) is in the conclusion but not in the premise.

14.1.1 The Logical Constant Sampled

The term sampled appears in the premise but disappears in the conclusion. This is what makes this form of argument a generalization—the premise is strictly about those individuals in the population that have been sampled, while the conclusion is generally about the population as a whole. We will treat sampled as a logical constant, like if–then, or, and not. Stylistic variants include visited, seen, observed, tested, polled, and experienced. When you can see that an argument is an inductive generalization, translate all of these stylistic variants to sampled.

EXERCISES Chapter 14, set (a)

Identify the term that is being used as a stylistic variant for sampled in each of these sentences, then paraphrase each sentence so that it displays correct form for the premise of an inductive generalization.

Sample exercise. Of the 100 people I asked, 53 said they are better off now than they were four years ago.

Sample answer. Sample solution. Asked is the stylistic variant. 53 percent of the sampled people say they are better off now than they were four years ago.

I’ve never had a piece of pie at the Country Kitchen that I didn’t think was delicious.
All of the websites I visited had site maps on the bottom of the landing page.
There has never been a documented case of a human attacked by a healthy wolf.
We began our study by randomly selecting 1,000 students enrolled in the college and interviewing them. It turned out that 820 of them said “yes” to the question, “Does it annoy you to be asked questions as part of a randomly selected sample?”
More than ten percent of America’s long-term coal miners who were x-rayed had black lung disease.
Only 5 of the 25 cars we saw driving in the car-pool lane today had more than one occupant.

Exercises Chapter 14, set (b)

List five stylistic variants for sampled that have not yet been introduced in the text, and make up an ordinary-language frequency statement (not necessarily in standard format) that uses it.

Sample answer: Rode on. All of the Metro buses I rode on had a bumpy ride.

14.1.2 The Margin of Error

The second difference between premise and conclusion is the + or – m of the conclusion, which represents the margin of error. Consider the following inductive generalization:

Fifty percent of the sampled voters favor Jones.
∴ Fifty percent (+/- 3 percent) of the voters favor Jones.

The 3 percent margin of error simply means that between 47 percent and 53 percent—inclusive—of the voters favor Jones. Professionals term 47 percent to 53 percent the confidence interval. That is, the conclusion would mean the same thing if it were stated in this way:

∴ The percentage of voters who favor Jones is somewhere in the range from 47 percent to 53 percent.

Notice that the margin of error strengthens the argument enormously. Without the margin of error, the conclusion would have been the much more precise 50 percent of the voters favor Jones. And this would have been false if the actual frequency of voters favoring Jones had turned out to be .53 or even .501. By including the margin of error in the conclusion, the conclusion turns out to be true with either result.

The margin of error is sometimes expressed more colloquially. When the premise in a casually expressed argument is, for example, Half of the sampled F are G, one way of including a margin of error in the conclusion is to say About half of F are G. Or when the premise is, for example, All sampled F are G, a margin of error is being incorporated when the conclusion is expressed as Almost all F are G. As you can see, about half and almost all are much more likely to generate a true conclusion than half and all.

Why, then, shouldn’t an arguer include the largest possible margin of error in every inductive generalization? For the simple reason that we need some degree of precision in the answers to most of the questions that inductive generalizations answer. A pollster would certainly be much more likely to have a true conclusion with the following argument:

∴ Fifty percent (+ or – 50 percent) of the voters favor Jones.

As long as anywhere from 0 percent to 100 percent of the voters turn out to be in favor of Jones, the pollster’s results are accurate. But the pollster would also quickly be unemployed; we don’t need professionals to tell us that between none and all of the voters favor a particular candidate. Even narrow margins of error can sometimes render an inductive generalization useless. One recent poll of citizens of Quebec concluded that 49.5 percent (+/- 3 percent) favored secession from Canada while 50.5 percent (+/- 3 percent) opposed it. The overlap produced by the 3 percent margin of error leaves us with an inconclusive result; [2] the margin would have to be reduced to less than one-half of a percent for this particular poll to be useful. The necessity of including even a 3 percent margin of error, in this case, renders the results useless.

This also applies to everyday life. Suppose one of my children is scared of monsters in the night. I turn on the light, check a few places in the room, find no monsters in the sampled places, and, remembering to include a margin of error, I say to my child, “I have concluded that almost no places in the room have monsters.” This would clearly not be satisfactory, for the situation requires a conclusion with much greater precision—namely, the very precise “No places in the room have monsters.”

It can be charitable to include a margin of error in your paraphrase, but only when loyalty allows it. If someone argues, “I’ve never witnessed a single rainy day in southern California, so I conclude that it absolutely never rains in southern California,” it would be disloyal to paraphrase the conclusion with a margin of error, even though it would make it more likely to be true:

No sampled days in southern California are rainy.
∴ Almost no days in southern California are rainy.

On the other hand, if someone says, “Half of my students tell me that they are planning to pursue an advanced degree, and I take that as a reliable indicator of the plans of students throughout the country,” then it seems proper to provide this charitable paraphrase:

Half of the sampled American college students are planning to pursue an advanced degree.
∴ About half of the American college students are planning to pursue an advanced degree.

Many inductive generalizations, for better or for worse, are like the rainy day argument above—they cannot be loyally paraphrased with any margin of error in the conclusion. This does not mean that such an argument fails to satisfy the correct form condition; rather, it simply means that it should be understood as including a margin of error of 0 percent.

n of sampled F are G. (Where n is any frequency, including 0 and 1.)

Exercises Chapter 14, set (c)

Provide a conclusion, in correct form for inductive generalization, for each of the premises in set (a) of the exercises for this chapter. Include a non-zero margin of error; don’t worry for now about whether the margin of error is too large or too small. (And don’t forget to drop the term sampled. )

Sample exercise. 53 percent of the sampled people are better off now than they were four years ago.

Sample answer. 53 percent (+ or – 10 percent) of the people are better off now than they were four years ago.

14.2 The Total Evidence Condition (1): Sample Size

As we have established, if an inductive argument is to be logical it is not enough that it satisfies the correct form condition. Correct inductive form makes the argument a candidate for logical success, but it can tell you nothing about how inductively strong the argument is. This is where the total evidence condition makes its entrance. Once we see that the conclusion fits the premises, we must then see how well it fits the total available evidence.

For an inductive generalization, when considering the total evidence condition the central question to ask is this: Is the sample representative of the population? Does the part of the elephant touched by the blind man feel like the rest of the elephant? Does the tasted spoonful of soup taste like the rest of the pot? Do the polled voters accurately reflect the views of the entire electorate? There is only one way to be completely sure—namely by sampling the remainder of the population. But this is generally not practicable; if you taste the rest of the soup, there’s none left for the dinner guests.

There are two things to look at when assessing whether the sample accurately represents the population: the size of the sample—it must be large enough—and the randomness of the selection process—every member of the population must have an equal chance to be included in the sample. Inductive generalizations that fail in one or both of these areas are sometimes said to commit the fallacy of hasty generalization. It is worth mentioning this fallacy, however, only because it reminds us of how easy it is to be satisfied with a sample that is not representative. The fallacy tells us nothing about the specific way in which the argument fails; for that reason, it is best to avoid the term and focus your evaluation on specific failures in measuring up to standards for size of sample and randomness of selection.

What makes the sample representative:

The sample must be large enough.
The sample must be randomly selected.

14.2.1 The Sample Must Be Large Enough

The first total-evidence question to ask is this: Is the sample large enough? No single size is right for every sample. Sometimes a sample of one should be enough. How many spoonfuls do you have to taste to decide if there is enough salt in the soup? But in other cases, 1,000 might be closer to the right number. Most market research and public opinion firms seem to interview roughly that number of people. And sometimes the really ambitious researchers go for gigantic samples (though, as we will see, this is almost always unnecessary). Dr. Alfred Kinsey, for example, who published the enormously influential volumes Sexual Behavior in the Human Male and Sexual Behavior in the Human Female in the mid-20th century, was convinced that he needed to collect 100,000 histories to have a representative sample of the population.

14.2.2 When a Sample of One Is Enough

We will proceed from here via a few simple rules of thumb; these tips will give you all that you need for most practical purposes to evaluate most inductive generalizations. (If you are thirsty for more, you may wish to read a book or take a course in statistics.) The first rule of thumb is this: for most inductive generalizations, you need a sample of either one or 1,000.

The way to decide whether it should be one or 1,000 is to ask the question, Is this an all-or-none property? With some properties, it is fairly clear that either all or none of the entire population has it. Saltiness of soup—assuming the pot has been stirred—is a good example; before you take a taste, it is reasonable to believe that if the taste is too salty, then the entire pot is too salty; but if it is not salty enough, then the entire pot is not salty enough. The properties too salty and not salty enough are in this case all-or-none properties.

We can look around us and discover many more everyday examples of all-or-none properties. Are you curious about what the morning edition of the Chicago Tribune reports about the snow conditions on the slopes of Vail? When you buy a copy and see that it reports excellent snow conditions, it is reasonable for you to conclude that this is what is reported by all the papers in that entire edition. That is, it is reasonable for you to reason as follows:

All sampled copies of the morning edition of the Chicago Tribune report excellent snow conditions at Vail.
∴ All copies of the morning edition of the Chicago Tribune report excellent snow conditions at Vail.

Reporting excellent snow conditions at Vail is likely to be an all-or-none sort of property for copies of the same edition of a newspaper; so a sample size of one is sufficient. You could buy a thousand copies from newsstands and newspaper boxes throughout Chicago and check them, just to be sure the sample is large enough to support your conclusion. Doing so would strengthen your argument a bit, since it would help rule out the remote possibility that the first copy was the result of some bizarre error or trick. But that possibility is typically so remote that the added strength of the 999 extra copies would be negligible. [3]

Calvin Coolidge, it is said, was once visiting a farm with some friends. When they came to a flock of sheep, one of the friends said, “I see these sheep have just been shorn.” Coolidge, famous for his caution, replied, “Looks like it from this side.” Coolidge was reluctant to generalize from the visible part—the sampled part—of each sheep to the whole sheep. But he didn’t really have to be so cautious. Given what most of us know, it is reasonable to believe that whether a sheep is shorn is an all-or-none sort of property; thus, even if we have sampled only one part of the sheep, if that part is shorn we can generalize to the whole sheep.

Inductive generalizations, however, are often criticized quite legitimately for relying on samples of one, or samples larger than one that are nevertheless too small. You would not, for example, interview merely one voter to find out which presidential candidate is preferred by the electorate, since favors candidate A is not typically an all-or-none property; we expect to find variety in the population with respect to this property. (The story is very different if you wish to find out which presidential candidate is favored by the electoral college of a single state; since they all vote the same way, depending on which candidate won the plurality of the state’s votes, you can generalize to them all if you know the vote of one.) Likewise, you should not—and if you are careful, you would not—make a decision about, say, someone’s honesty based on a single interchange with that person. Someone’s honest behavior in your first brief conversation may or may not be representative of that person’s behavior in general. A sample much larger than a single meeting is necessary for a logical argument about general behavior—this is one of the reasons we typically date before marriage.

If the inductive generalization is conducted scientifically—by a public opinion poll, say, or an experiment on rats or human subjects—then typically you will find that the property is not all-or-none. If the scientists had believed it to be an all-or-none property, they would not have gone to the trouble and expense required to construct a large sample so carefully.

Exercises Chapter 14, set (d)

For each of the simple arguments below, clarify it in standard format, identify the relevant property, and state whether it is probably, for this population, an all-or-none property.

Sample exercise. One camper to another, taking a thermometer out of a pot of water boiling over the fire: “See, the thermometer shows 99 degrees Centigrade. So that’s the temperature at which water boils at an altitude of 5,000 feet.”

Sample answer.

All sampled water at an altitude of 5,000 feet boils at 99 degrees Centigrade.
∴ All water at an altitude of 5,000 feet boils at 99 degrees Centigrade.

Property is boils at 99 degrees Centigrade. It is probably an all-or-none property, thus a sample of one should be enough.

One 7-Eleven shopper to another, holding up a can of coffee: “I can see from this can that 13-ounce cans of Folger’s coffee cost $3.99 here.”
That driver almost ran me off the road. It’s obvious that people who live in this city are terrible drivers.
You have trouble doing long division? Then you’re not very intelligent, are you? (Hint—make the population opportunities to show intelligence. )
How do I know that any copy of her brand new book that you pick up will be long? I just read all 750 pages.
I’ve known two other people from Syracuse, and they were both of Norwegian descent. So I guess most people from Syracuse are Norwegian.
That ant bit me and left an angry red welt on my leg. I’m not going near the rest of them.

14.2.3 When a Sample of 1,000 Is Enough

When the one-or-1,000 rule of thumb is applied and the property is not all-or-none, you can assume for most purposes that a sample of 1,000 is sufficient for a logically strong argument—assuming the sample is randomly selected. This is well illustrated by public opinion polls and marketing surveys, which almost always have samples of roughly that size. But there is nothing magical about the number 1,000; its sufficiency depends on several things—most notably the margin of error. Whether a random sample of 1,000 is big enough depends on whether the margin of error is at least 3 percent. If it is impossible to collect a sample of 1,000, then the arguer must settle for a larger margin of error or for a logically weaker argument.

Let us look at this in a more general way. We have already seen two things that can increase the logical strength of an inductive generalization. The larger the margin of error, the stronger the logic of the argument. And the larger the sample size—assuming it is randomly selected—the stronger the logic of the argument (though, as we have already seen, increases in sample size after a certain point are only marginally helpful). This suggests another rule of thumb: if the margin of error increases appropriately as the sample size decreases, the logical strength of the argument remains steady. The bigger the margin of error, the smaller the necessary sample size. The reverse is likewise true: the smaller the margin of error, the larger the necessary sample size.

Statisticians can establish for any sample size (assuming the sample is randomly selected) the margin of error that can be confidently assumed. Here are some useful points along the continuum:

The Margin of Error per Sample Size


10	+/- 30 percent
100	+/- 10 percent
500	+/- 4 percent
1,000	+/- 3 percent
2,000	+/- 2 percent

This means that a voter opinion survey of 1,000 people can provide the basis for a strong inductive generalization so long as the conclusion allows for a margin of error of at least 3 percent. Suppose this is the premise:

1. Fifty percent of the sampled voters favor Jones.

A random sample of 1,000 is large enough to support this conclusion:

But if the random sample were only 100, the logic of the induction would be equally strong only if the argument concluded that from 40 percent to 60 percent favored Jones. If the random sample were 10, then the conclusion would have to be that from 20 percent to 80 percent favored Jones. If, however, it were as large as if 2,000, then the conclusion could be narrowed to the assertion that 48 percent to 52 percent favored Jones.

Some arguments do not need a high level of precision. Suppose I am interested in providing venture capital to fund a specialty candy store in the local shopping mall, and I determine that at least 10 percent of the shoppers will have to buy something in the store if it is to succeed. I randomly ( really randomly) interview 10 shoppers and find that 6 of them would have bought candy from my store. From this I can conclude that 60 percent (+/- 30 percent) of the shoppers would buy candy from the store (I take this directly from the preceding table of sample sizes), that is, anywhere from 30 percent to 90 percent. This is well above my cutoff point of 10 percent, so greater precision is not necessary. Again, in general, the less precision needed in the conclusion, the smaller the sample that is needed.

Rules of Thumb for Judging Sample Size When the Sample Is Randomly Selected

One is enough when the property is all-or-none.
1,000 are enough when the property is not all-or-none and the margin of error is at least 3 percent.

Exercises Chapter 14, set (e)

For each sample that is described write a conclusion with an appropriate margin of error.

Sample exercise. A random sample of 500 pairs of socks put into clothes dryers showed that one-fourth of the pairs lost one member by the end of the cycle.

Sample answer. Twenty-five percent (+/- 4 percent) of pairs of socks put into clothes dryers lose a member by the end of the cycle.

In a random sample of 10 owners of Subarus in the most recent model year, 5 of them were “extremely pleased” with their car.
Four percent of 2,000 randomly sampled American homeowners said they preferred renting.
In a random sample of 100 days in Atlanta, on 7 of them unhealthful levels of ozone were in the air.
One-tenth of a random sample of 1,000 mosquitoes captured in a Florida swamp were carrying the virus that causes encephalitis.
In a random sample of 1,000 Texas adults, 483 believe the state sport should be rodeo.
In a random sample of 500 television episodes from 50 years of television history, one-third of them depicted at least one murder.
Eighteen percent of the 500 streetlights sampled at random in Manhattan were out of order.
Of 2,000 randomly sampled American Express cardholders, 1,609 were pleased with their customer service.

14.2.4 Population Size

Although the size of the sample is very important, the size of the population has very little to do with the logical strength of the argument. This may initially strike you as contrary to common sense. But note that common sense does not tell you that you must take a bigger taste if you have a bigger pot of soup, assuming it is properly stirred.

If the population is very small, population size can matter. Suppose you have 1,000 trees in your apple orchard and you want to sample them to learn how many trees are diseased. Being diseased is not likely to be an all-or-none property in an apple orchard, so our one-or-1,000 rule tells us to sample 1,000 trees. Suppose you do that and find that 160 of them are diseased. You no longer need to generalize; you have sampled the entire population, you have found 16 percent to be diseased, and no inference from sample to population is necessary. Small populations matter because they make inductive generalizations unnecessary.

Suppose, however, that owing to the cost and difficulty of testing the trees, you cannot randomly check more than 500 of them; you do so and find that 71 of the sampled trees are diseased. At this point the best thing you can do is refer to the preceding sample size table. If you want a logically strong argument, you must conclude that 14.2 percent, plus or minus 4 percent, are diseased—the same as if your sample of 500 had been from an orchard 10 times larger. We find the same phenomenon in polling practices. If there are 150 voters in our hamlet, we can interview every voter and avoid the generalization. If there are 15 million voters, which is roughly the case in Canada, pollsters require a random sample of 1,000 to conclude that half the voters, with a margin of error of 3 percent, favor Jones. And if there are 150 million voters, which is roughly the case in America, they still require a random sample of 1,000 to conclude that half the voters, with a margin of error of 3 percent, favor Jones.

Why does this work? If 16 percent of the trees are diseased, then for each randomly selected tree there is a 16 percent chance, or a .16 probability, that it is diseased. This is true whether we are talking about 16 percent of 1,000 or 16 percent of 10,000. And if exactly half of the voters favor Jones, then—whether we are talking about half of 10 million or half of 100 million—there is a 50 percent chance, or a .50 probability, that each randomly selected voter favors Jones. It is this property of each member of the sample that governs the behavior of the sample as a whole.

For practical reasons it may be more difficult to get a random sample when the population is far larger. Ten thousand trees or 100 million voters may be spread over a huge geographical area, making it impossible to give every member of the population an equal chance of being included in the sample. Thus, sample size or margin of error may be strategically increased to offset these practical difficulties (as we will see in the next section). But these adjustments are directly due to lack of randomness, and only indirectly due to population size.

14.2.5 Logical Strength and Confidence Level

Exactly how logically strong is the inductive generalization that begins 50 percent of the sampled voters favor Jones, assuming that it is based on a random sample of 1,000 and a margin of error of 3 percent? How much support does the premise provide the conclusion? This can be answered quite precisely: the probability of the conclusion, based on this premise and the relevant background evidence, is .95.

Suppose the population of voters in the Jones poll numbers 10 million, and suppose that exactly 5 million—that is, 50 percent—favor Jones. Statisticians tell us that if we took 20 different random samples of 1,000 from that population of 10 million, 19 of those 20 times the number in the sample that favored Jones would be in the range of 47 percent to 53 percent (that is, 50 percent +/- 3 percent). Since the true conclusion, namely, 50 percent ( + / – 3 percent) of the voters favor Jones, would occur 19 out of the 20 times, or in 95 percent of the cases, its probability of success is .95. We may have gotten it wrong this time, but that would mean that this is the 1 time in 20 it would happen.

If, on the other hand, we took 20 different random samples of only 10 from that population, 19 out of 20 times the number in the sample that favored Jones would be in the range of 20 percent to 80 percent. To get the same .95 probability of success with such a small sample, the conclusion must be 50 percent ( + / – 30 percent) of the voters favor Jones.

Professional researchers would typically term this a confidence level of .95. Confidence level, however, is just another expression for the probability of the conclusion, given the truth of the premise and given the relevant background information. That is, it is just another expression for logical strength. (It is not the level of confidence that you do have, but the level of confidence that you rationally ought to have.) Professional researchers tend to aim for arguments with a .95 probability, and we will typically refer to arguments that achieve this level of probability as very strong.

No rule says that when the probability is .95 you should believe the conclusion. We are only talking about the argument’s logic. There must also be a very high probability that the premises are true before you accept the argument as sound. Nor does any rule say that when you do accept such an argument as sound and when you do believe the conclusion that you should act with confidence on it. If the argument’s conclusion has to do with whether a rope bridge over a treacherous waterfall is able to support you, you may quite reasonably turn around and go home unless you can be given much better than a 19 out of 20 chance of survival. But if the conclusion has to do with whether a black speck on the table is a fly or an imperfection in the surface, you may quite reasonably attempt to brush it away even if the confidence level is considerably lower than .95.

The vocabulary of logical strength, probability, and confidence level can also be applied to the other sorts of arguments we have covered. In deductively valid arguments, for example, the conclusion would be true every time you considered the premises; thus, they confer a probability of 1.00 on their conclusions—that is, they have a confidence level of 1.00. And consider frequency arguments, such as this one:

Sixty-seven percent of the marbles in the clay pot are red.
The marble I’ve just taken in my hand is a marble in the clay pot.
∴ The marble I’ve just taken in my hand is red.

Assuming I have no relevant background evidence except for the frequency expressed in the first premise, we can say that the conclusion will be true 67 percent of the times that I take a marble in my hand. Thus, the conclusion, given those premises and that background evidence, has a probability of .67—and the argument has a confidence level of .67.

Exercises Chapter 14, set (f)

Create a brief argument of the sort described and with the degree of logical success described. Explain.

Sample exercise. Inductive generalization, no support at all.

Sample answer. All of the Italian restaurants I have visited have served pasta, so it follows that only Italian restaurants serve pasta. (No support because does not satisfy the correct form condition.)

Inductive generalization, .95 probability (very strong).
Frequency argument, .55 probability (very weak).
Singular affirming the antecedent, 1.00 probability (valid).
Frequency argument, .50 probability (no support).
Singular denying the antecedent, .50 probability (no support—note that a probability below .50 would support the falsity of the conclusion).

14.3 The Total Evidence Condition (2): Random Selection

14.3.1 random selection.

To review, we are considering how to evaluate the logic of inductive generalizations. We are assuming that the correct form condition is satisfied and we are focusing on the total evidence condition. For the total evidence condition to be satisfied, recall, the key question is whether the sample accurately represents the population. This can be divided into two questions: whether the sample is large enough, and whether the sample has been randomly collected. We now turn to the second question.

To say that sample selection is random, for the practical purposes of this text, is to say that every member of the population has had an equal opportunity to be included in the sample, so that exactly the relevant variations of the population might be proportionately represented. This is an important definition, for it differs from the way we ordinarily use the term. There would be nothing unusual about my saying, “I randomly interviewed 30 people at the bus station to find out what people in the city think of rapid transit.” This, however, is not the sort of randomness that we are looking for in evaluating inductive generalizations. In this relaxed use of the term, random simply means indiscriminate, or without any special principle of selection. But notice that not everyone in the city had an equal opportunity to be included in the sample—only those who happened to be at the bus station. This means that relevant variations of the population have almost certainly been omitted from the sample; for example, people who never ride the bus, and so are excluded from the sample, probably tend to have views on this subject that differ from those who ride it. In short, the randomness that we are looking for is not indiscriminate randomness; it requires carefully considered principles of selection.

An ideal way to get a perfectly random sample would be to list all the members of the population, run the names through a computerized randomizing program (or shake them thoroughly in a giant hat, or put each name on a surface of a huge many-sided fair die), and sample the first 1,000 that are selected. But this is almost never something that works in real life. It would be prohibitively expensive to do this if, say, you were generalizing about voter preferences across the entire American population. And it would simply make no sense if you were, say, generalizing about pollution throughout an entire river. (How would you list all the potential beakers of water that make up the river?)

Professionals usually find it simpler to achieve randomness by a technique called stratification. They make an informed judgment regarding which subpopulations are likely to differ from the larger population in the frequency with which they exhibit the property in question. They divide the population proportionately into these smaller populations, or strata, and sample at random from each stratum. Suppose, for example, the population is registered voters in the state of North Carolina and the property is prefers the Republican candidate in the North Carolina gubernatorial election. Voter preference is likely to vary according to factors such as party affiliation, ethnicity, economic status, and gender. So the pollsters must ensure that they have randomly selected, for example, Republicans, African-Americans, welfare recipients, and women in sufficient numbers so that their share of the sample matches their share of the population of North Carolina’s registered voters. Voter preference is not likely to vary, however, according to astrological sign, so there is no need to be sure that a Scorpio stratum is included in the sample.

Exercises Chapter 14, set (g)

For each statement in set (e), list (i) the population, (ii) the property, (iii) two relevant variations in the population, and (iv) one irrelevant variation.

Sample answer. Population: pairs socks put into clothes dryers. Property: lost one member by the end of the cycle. Relevant variations: size of load, time of cycle. Irrelevant variation: brand of socks.

14.3.2 Random Mistakes

Our purpose in this textbook is not to design samples but to evaluate arguments. This section will help you in detecting ways in which a sample might fail to be randomly selected and thereby contribute to an unsound argument.

Sometimes you can see that a relevant variation has been omitted without knowing the exact sampling process that was used. If you knew that 75 percent of those in the sample were men, and the question was whether Americans thought that women were treated equally in the workforce, then you would know there was a problem with the sample; attitudes on this vary with gender, so the genders must be equally represented. If, on the other hand, the question were whether baseball fans favored the designated hitter rule, you probably would not know whether there was a problem with the sample. It may well be that 75 percent of all baseball fans are men, in which case they would turn up with this frequency in a random sample.

Often you simply have no details about the sample, in which case your approval of the argument’s logic may depend on whether you trust the person or organization that collected it. The Chapters 8 and 9 guidelines for appeals to authority are directly pertinent here. Was the research done by a credible organization? Is there no sign of sponsorship by a business that has an interest in a certain outcome? Is the prior probability of the outcome reasonably high? Yes answers to all of these questions count in favor of the argument.

There are, however, a few tips that can reliably tell you when a sample is not randomly selected. Grab sampling, for example, is the process of including in your sample whatever members of the population happen to come your way. This is the method used in the bus station case; it is easy to do, but it rarely provides a representative sample. In The De-Valuing of America, William Bennett recounts the use of such a technique by a department chair at a prestigious university, who remarked the day after the 1980 presidential election: “I voted for Carter. Most of my colleagues voted for Carter. And a few voted for Anderson. But Reagan got elected. Who the hell voted for Reagan?”

The following Los Angeles Times story includes an obviously flawed grab sample:

The Water Quality Control Board is considering imposing fines of $10,000 against the City of Los Angeles for each major discharge of raw sewage. But Harry Sizemore, assistant director of the city Bureau of Sanitation, insists that the water in the ocean does not cause disease. “I swim there,” he said. “And several members of our bureau are avid surfers who use the area. None of us has ever caught any diseases from it.”

This argument has several defects besides its dependence on a flawed sampling procedure. For example, there is some reason to distrust the reports of this particular group—and thus, reason to doubt the truth of the premise. Further, the sample is a very small one. And we would prefer an analysis of a random sample of the water itself rather than a random sample of those who have been in the water. But the relevant point here is that Sizemore has not provided us with a random sample of those who have been in the water. It is a grab sample, made up of whomever Sizemore happened to talk to at the office, and thus there is no reason to think that it is representative.

Snowball sampling, a close relative of grab sampling, is the process of adding new members to the sample on the basis of their close relationship with those already included (thus gathering members in the same way that a snowball gathers snow as it rolls along). I have already mentioned the highly publicized studies on sexual behavior conducted by Alfred Kinsey in the 1940s and 1950s. Kinsey frequently selected new interview subjects by asking his interview subjects to refer him to their friends and acquaintances. Given that he had a special interest in talking to those whose sexual practices were not considered mainstream, and given that friends and acquaintances of those who were not in the sexual mainstream were themselves somewhat likely to be out of the mainstream, this snowball sampling produced significant distortions in his sample. True, Kinsey collected an enormous sample. But, due to his snowball technique, the magnified sample size magnified the distortion.

Self-selected sampling is probably the most common, and most insidious, error. This occurs when members of the population decide for themselves whether to be included in the sample. Before we stray too far from Alfred Kinsey, note this Psychology Today review of a similar but more recent study:

Love, Sex, and Aging is a report of a survey of 4,246 Americans aged 50 and older—the largest sample of older persons about whom detailed sexual data exist. It is composed entirely of volunteers who responded to an ad in Consumer Reports. The authors of the book say, “We are confident that many or most of our findings apply to a very broad segment of Americans over 50,” and present their findings in that spirit. Item: two-thirds of the women and four-fifths of the men 70 or older are still sexually active. Grandma, Grandpa, you couldn’t! You don’t!

Let’s begin by treating the argument a bit more fully. For simplicity, let’s clarify only the argument about men:

Eighty percent of the sampled men aged 70 or older are still sexually active.
∴ About 80 percent of men aged 70 or older are still sexually active.

The frequency is 80 percent, the population is men aged 70 or older, and the property is still sexually active. I’ve charitably included an informal margin of error ( about 80 percent) in the conclusion, which seems warranted by the imprecise way the authors express their conclusion (“most of our findings apply to a very broad segment of Americans”). I will take the premise to be probably true, since I have no reason to doubt the truthfulness of the authors and no compelling reason to doubt the word of those who submitted the survey (though it is possible that those who submitted the surveys either overstated or understated the extent of their sexual activities).

This brings us to an evaluation of the argument’s logic. It clearly satisfies the correct form condition, so we can move on to the total evidence condition. Is the sample large enough? It is hard to say, since the excerpt merely states that 4,246 people over the age of 50 responded to the survey; but the argument we are considering is based only on the surveys submitted by men over the age of 70. Let’s suppose there are a few hundred in this category, thus probably the sample is large enough to support the vague “about 80 percent” of the conclusion.

But is the sample randomly selected? Certainly not. As the passage states, the sample is made up of those who voluntarily responded to a survey in Consumer Reports. This filters out all of those who read Consumer Reports but are not interested enough in sex to be interested in filling out a survey on the topic. It also filters out a large group of elderly people who ignore Consumer Reports because they can’t afford most of the items described in the magazine. These people are also unable to afford top medical care and for that reason they are probably less healthy and less interested in sex. In short, the sample is self-selected and thus grossly unrepresentative.

For that reason alone, the logic of the argument is very weak. There is no problem with the argument’s premise or with its conversational relevance, but because of its weak logic it is clearly unsound.

Finally, dirty sampling is the contamination of the sample—usually unintentional—by the sampling process itself. If you are examining your newly laundered shirts with muddy hands, your sample shirts will be muddy. Even if you have made no other sample-selection mistakes, this sample cannot support the general conclusion that all your newly laundered shirts are muddy. This is a failure of randomness, since in a randomly selected sample, exactly the relevant variations of the population are proportionately represented. Introducing mud is introducing a relevant variation that is not in the population.

Dirty sampling does not necessarily introduce dirt, but it does introduce a change in the sample that makes the sample relevantly different from the population. Suppose you are a somewhat absent-minded naturalist and wish to learn more about the eyesight of a tiny species of shrew that is nearing extinction. You use a strong light to see their eyes better, and find that all shrews in your sample have extremely small pupils relative to the size of their eyes. Your sampling procedure, of course, is dirty, since in mammals strong light typically causes the pupils to contract. The sampling process cannot be considered random, and the premise can provide no support to the conclusion.

Exercises Chapter 14, set (h)

For each of these passages, clarify the inductive generalization and then answer, with a brief explanation, the two total evidence questions.

Sample exercise. “The people, it seems, have declared California Republican Ronald Reagan the winner of the Reagan–Carter debate. Nearly 700,000 people paid 50 cents each to take part in an instant ABC News telephone survey following the presidential debate, and by a 2-to-1 margin they said Ronald Reagan had gained more from the encounter than Georgia Democrat Carter. ABC said that of the callers who reached one of the two special 900-prefix numbers during the 100 minutes following Tuesday night’s debate, 469,412 people or 67 percent dialed the number designated for Reagan and 227,017 or 33 percent dialed the one assigned to Carter. The network said an especially heavy volume of calls was recorded from ‘Western states’ but had no more precise breakdown immediately.”—from the Associated Press

Sixty-seven percent of the sampled Americans considered Reagan the winner of the debate.
∴ About 67 percent of Americans considered Reagan the winner of the debate.

The sample is easily big enough (by 700 times). But it is not randomly selected. It was self-selected, with more Democrats (who would have favored Carter) filtered out because they are not as able to afford the 50 cents and with more non-Westerners (who would have been less likely to favor the Californian Reagan) filtered out because they were in a later time zone and had gone to bed.

21 of 30 students in an English 101 course at the local community college expressed doubt that the degree they were working toward would actually get them a good job. From this it seems reasonable to conclude that the majority of the students at the school don’t have much faith in the practical value of their education.
Only 25 percent of 1,000 residents of Manhattan polled at a free concert in Central Park said they would support privatizing the park and instituting a mandatory fee for entrance. The sample would seem to reflect the attitude of New Yorkers in general.
You are in charge of quality control for a pharmaceutical company, and part of your job is to run a laboratory that collects random samples of your company’s drugs each month and examines them carefully for purity. One month your lab obtains a startling result: 60 percent of the sampled drugs are impure. You alert the company president (and, of course, the public relations officer) that over half of that month’s product is tainted. (Meanwhile, one of your lab technicians inspects the beakers used for pre-examination sample storage and discovers that due to a change in laboratory cleaning protocol this month, a microscopic chemical residue is left on the beakers after cleaning. Minute amounts of this residue have commingled with many of the drugs, causing the impurity.)
In 1936, in the midst of the Great Depression, the Literary Digest randomly selected 10 million names from phone books across the country and mailed them sample ballots for the upcoming presidential election between Republican Alf Landon and Democrat Franklin Delano Roosevelt. About 2 million of the ballots were returned and, based on the results of that sample, the magazine predicted confidently that Landon would win by a clear majority. (Postscript: Roosevelt won with 60 percent of the popular vote, and the Literary Digest, having lost all credibility, ceased publication soon after.)
An elderly woman overheard speaking to her friend: “Recently I drove through a small ‘art-colony’ village in Pennsylvania, which is normally frequented by tourists. I got the shock of my life when I saw about 75 young people all dressed exactly alike—in blue denim! I wondered if there had been a prison break, or an invasion of the Union Army. What is it with our young people? They have about as much individuality as connected sausage links. They all look alike. Same dress, same jeans, same long straight hair—it’s hard to tell one from the other.”
Most of the kids in this remote, rural high school in Grants, New Mexico, have only television to provide them with their images of big cities. Paul Sanchez confesses that he hates what he has seen of New York on television. As part of a class assignment, he writes: “New York seems like a corrupt place. Crime seems to rule. I am not a person who is easily intimidated but TV did it.”— TV Guide
Americans support the idea of letting children attend public schools of their choice. The public favored by a margin of 62 percent to 33 percent allowing students and parents to choose which public schools in their community the students attend. Officials said the Gallup-Phi Delta Kappa poll is the most comprehensive survey of American attitudes on educational issues since the series began in 1969. This year, Gallup interviewers asked a selected sample of 1,500 American adults 80 questions. The margin of error was 3 percentage points.—Associated Press
I have a master’s degree in mathematics and was well thought of by my professors. I am working as a computer programmer, and my coworkers, supervisors, and users admire my abilities. I scored in the upper 2 percentile on college entrance tests, usually in the upper percentile for mathematics and biology. However, I would probably score poorly on the Kaufmans’ test because I have a poor short-term memory. It sometimes takes me several months to learn my telephone number and address when I move. I find it hard to believe there is a strong correlation between short-term memory and the ability to think logically.—Letter to the editor, Science News

Four Ways Samples Can Fail to Be Randomly Selected

Grab sampling
Snowball sampling
Self-selected sampling
Dirty sampling

14.4 Evaluating the Truth of Premises about Sampling

Everything we said in Chapter 9 about the truth of premises applies to the premise of an inductive generalization. Of all the points covered there, the most important for present purposes is the point about dependence on authority. Usually, whether you accept the premise of an inductive generalization is a matter of whether you believe the sampler. Did the person really sample that population and find that property with that frequency? Make this decision in the same way you make any other decision about whether to rely on an authority.

14.4.1 Misunderstood Samples

In misunderstood samples, the method used for collecting information about the sample is not entirely reliable. This results in a misunderstanding of the sample’s properties, rendering the premise false. A. C. Nielsen, who established the Nielsen ratings system for television shows, began his career doing market research for retailers. One of his early accounts was Procter & Gamble, for whom he did a survey on soap. He carefully constructed his sample, did his survey, and returned with results that were drastically at odds with the Procter & Gamble sales data. The main discrepancy was that sales of Lux bar soap were lagging badly, even though huge numbers of those surveyed said they used Lux regularly. Nielsen was perplexed until he realized that Lux had the image of a soap for the well-to-do; people wanted to impress the interviewer, and thus said they used Lux whether they did or not. His sample was representative—it was large enough and it was randomly selected. The problem was with the premise, which stated that the sampled consumers used Lux with a certain frequency. They did not; the premise was false. The lesson for Nielsen was to find a more reliable way of determining what people really think.

In an old Frank Capra movie called Magic Town, James Stewart plays a public opinion pollster who doesn’t have the resources to compete with the major organizations like Gallup and Harris. He happens upon a small town that perfectly reflects the variations found in the American public in general. He regularly solicits their opinions by disguising the interviews as casual conversation and produces astonishingly accurate results. But his love interest, a journalist played by Jane Wyman, finds out about his technique. Choosing truth over love, she writes a widely distributed feature article about the town. With the appearance of the article, one town councilman snorts, “In one week’s time I wouldn’t give the wart off my nose for anybody’s opinion in this town.” And he is right. Soon the townspeople are setting up booths for the dispensing of their opinions and affecting pompous airs. Aware of their importance, they take themselves too seriously, and the next poll is a disaster. Their views haven’t ceased to be representative; rather, they are now expressing views that they think they ought to have instead of their real views.

There are things that can be done to encourage a misunderstanding of the sample. In Chapter 4 we saw the power of slanted language; two questions might have the same cognitive content, but, cloaked in very different language, might generate very different reactions. When Jerry Falwell was the leader of the ultraconservative Moral Majority, he once took out a full-page advertisement asking readers to return their answers to several questions. One of the questions was this:

Are you willing to trust the survival of America to a nuclear freeze agreement with Russia, a nation that rejects on-site inspection of military facilities to ensure compliance?

It is very hard to say “yes” to the question. But if 90 percent of the respondents said “no” and Falwell reported, say, that 90 percent of the sampled Americans oppose a nuclear freeze agreement with Russia, it is likely that the premise would be false. Even if 90 percent said they opposed it, many would not have been expressing their true views.

At the same time, creative researchers often find ways of overcoming obstacles to understanding the sample. One study by a market research firm asked people to name their favorite magazine, knowing that they were likely to cite magazines that might impress the interviewer, such as Harper’s or the New Yorker. The surveyors, out of gratitude for the interview, then offered each person a free copy of any magazine of the person’s choice. The frequency with which they chose People and TV Guide was much higher than the frequency with which they admitted it was their favorite. You can imagine which data the researchers used as the basis of their report.

Because people’s attitudes are easily hidden they are easy to misunderstand. Misunderstanding of samples is not necessarily limited, however, to people’s attitudes. In principle, any sample can be misunderstood; but the more hidden the property, the greater the opportunity for misunderstanding. I am less likely to misunderstand if I am sampling the weather in my backyard or the number of autos on the freeway. But I may start to slip if I am sampling the weather in China or the number of microparticles in auto emissions on the freeway.

There is no special reason to think that a misunderstood sample is also a dirty sample. As you scrutinize the auto emissions through your microscope, dirt on the lens may lead you to misunderstand the sample and so to offer a false premise about it. But it is not a dirty sample—and thus not unrepresentative—until the dirt falls from the lens and into the microparticles.

Exercises Chapter 14, set (i)

For each of these passages, clarify the inductive generalization and then evaluate the truth of the premise, with a special view to whether and how the sample has been misunderstood.

Sample exercise. One study showed that, based on their own report, 80 percent of the population is above average in intelligence.

Eighty percent of the sampled population is above average in intelligence.
∴ About 80 percent of the population is above average in intelligence.

The premise is probably false. (Not certainly, since we are not told the sampling process, and it is possible that the sampling was not random, but was done, say, at a reunion of college graduates.) If people are asked if they are above average in intelligence, they will usually say they are (and probably believe that they are) even if they are not. So there is no reason to accept the premise.

Across the board, reputable polls in 2016 estimated Trump’s level of support to be around 40%. Yet when the votes came in, he received over 46% of the votes. (Consider the “shy Trumper” view that many voters knew that supporting Trump was socially undesirable and thus did not admit it to pollsters.)
According to her, the whole world is rosy. You’d expect her to think that, since she’s always looking at it through rose-colored glasses.
A study by the fitness club study showed that 95 percent of its customers looked better after two months in its program. Subjects were asked to decide whether the customers looked better before or after based on “before” and “after” photographs supplied by the fitness club. (Scrutiny of the photographs indicates that in the “after” pictures the lighting was better and the customer had on more makeup, better clothes, and a bigger smile.)
Two University of Texas at Austin sociologists, David A. Snow and Cynthia L. Phillips, tested 1,125 students to see whether they were primarily concerned with themselves or society—with “impulse” or “institution,” as the researchers put it. Eighty percent saw themselves guided by their own “feeling, thought, and experience.” Only 20 percent saw themselves guided by “institutionalized roles and statuses.”— Psychology Today

14.5 Complex Arguments

Complex arguments, as we have seen, are nothing more than chains of simple arguments. If you can clarify and evaluate simple ones, you can do the same for complex ones. There is one fairly common sort of chain, however, that includes an inductive generalization and is worth considering here.

Sometimes, especially in informal arguments, we move from a statement about a sampled portion of a population to a conclusion about another member of the same population. I might argue, for example, “Every Japanese car I’ve ever owned has been well-built, so that Toyota is probably well-built.” Some would create a special category for such an argument; some logicians, for example, term it a singular predictive inference. Others might quite naturally take it to be an argument from analogy, in which that Toyota is argued to be analogous to every Japanese car I’ve ever owned. (See the next chapter for more detail on arguments from analogy.) But, as we saw briefly in Chapter 11, it is probably most useful to clarify it as a complex argument, made up of an inductive generalization followed by a singular categorical argument (or, in related cases, followed by a frequency argument). The clarification, then, would look something like this:

All sampled Japanese cars are well-built.
∴ [All Japanese cars are well-built.]
[That Toyota is a Japanese car.]
∴ That Toyota is well-built.

The inference to 2 is an inductive generalization, while the inference from 2 and 3 to C is a singular categorical argument.

Using reasoning of this sort, the FBI makes detailed profiles of criminals, interpreting evidence left at the scene in the light of their extensive records of similar crimes. In one sensational case a white female murder victim, naked and mutilated, was found in the Bronx. Agents at the FBI concluded that the killer was white, because in the overwhelming majority of mutilation murders, the killer is the same race as his victim. They further concluded that the murderer was in his mid-20s to early 30s, because the crime scene demonstrated a kind of methodical organization and such organization made an impulsive teenager or someone in his early 20s an unlikely suspect. An older man would likely have been jailed already, as the urge to commit brutal sex murders tends to surface at an early age, and the chances that a person could commit a number of such murders over a span of years without being captured would be slim. In this way, the FBI put together a detailed portrait of the killer and quickly found and convicted him.

This reasoning includes the sampling of hundreds of cases of mutilation murders; it also includes the application of that experience to a specific case. The following clarification captures one of the many similar complex arguments contained in the passage:

Almost all sampled mutilation murderers are the same race as their victims.
Almost all mutilation murderers are the same race as their victims.
The Bronx murderer is a mutilation murderer.
∴ The Bronx murderer is the same race as his victim.

It can now be evaluated in two parts—the first as an inductive generalization, the second as a frequency argument.

Exercises Chapter 14, set (j)

For each of these passages, clarify and evaluate the complex argument.

Sample exercise. “‘My name is McGlue, sir—William McGlue. I am a brother of the late Alexander McGlue. I picked up your paper this morning, and perceived in it an outrageous insult to my deceased relative, and I have come around to demand, sir, WHAT YOU MEAN by the following infamous language: “The death-angel smote Alexander McGlue, and gave him protracted repose; he wore a checked shirt and a number nine shoe, and he had a pink wart on his nose. No doubt he is happier dwelling in space over there on the evergreen shore. His friends are informed that his funeral takes place precisely at quarter-past-four.”

“This is simply diabolical. My late brother had no wart on his nose, sir. He had upon his nose neither a pink wart nor a green wart, nor a cream-colored wart, nor a wart of any other color. It is a slander! It is a gratuitous insult to my family, and I distinctly want you to say what do you mean by such conduct?

“‘. . . How could I know,’ murmured Mr. Slimmer, ‘. . . that the corpse hadn’t a pink wart? I used to know a man named McGlue and he had one, and I thought all McGlues had. This comes of irregularities in families.’”—Max Adeler, “The Obituary Poet”

All sampled McGlues have pink warts on their noses.
∴ All McGlues have pink warts on their noses.
Alexander McGlue is a McGlue.
∴ Alexander McGlue has a pink wart on his nose.

EVALUATION OF ARGUMENT TO 2

Premise 1 is probably true (in the story—given the silly nature of the story); no special reason to doubt Slimmer’s report.

Extremely weak. Satisfies correct form condition for inductive generalization. But a sample of one is insufficient, since having a pink wart on one’s nose is not an all-or-none property for families.

Unsound due to weak logic.

EVALUATION OF ARGUMENT TO C

Premise 2 is certainly false; not only is it not supported by the argument provided, but the story gives evidence that Alexander is a counter example.

Premise 3 is probably true—given context of story—no reason to doubt it.

Valid singular categorical argument.

Unsound due to falsity of premise 2.

A recent survey of 500 owners of golden retrievers indicated that 95 percent of them considered their dog to be well behaved with children. I think I’ll get this golden retriever, then—since it should be good with my kids.
Two educational psychologists at Temple University analyzed the instructor evaluation ratings done by 5,878 students at Temple, matching the ratings with the grades that the students had predicted for themselves. They found that in most courses, evaluations seem to be based on a variety of factors that generally outweigh the matter of just getting a good grade—that teachers can’t significantly affect their scores by leading students to believe that they will get good grades. So, your professor in this class shouldn’t expect assurances of good grades to inflate your instructor evaluation.

Exercises Chapter 14, set (k)

Clarify and evaluate the following arguments.

Most teenage girls now aspire to professional occupations, such as doctor or lawyer, according to a report by Helen Farmer, a psychologist at the University of Illinois. Farmer queried 1,234 9th and 12th graders from nine Illinois schools. By way of contrast, less than half of the boys had similar aspirations.—Associated Press.
“I’m grateful that CBS still carries the ‘Bugs Bunny/Road Runner’ show, that collection of Warner Bros. animated classics. The only catch is that occasional bits of cartoon ‘violence’ have been trimmed away by the network. This strikes me as unnecessary and downright silly. After all, I watched these cartoons without cuts when I was a kid, and I turned out fine. Or at least OK.”— TV Guide
“I watched ‘PBS NewsHour’ one night last week and I watched the ‘ABC Evening News’ an hour later. With about 28.5 minutes more than the 21 actually delivered by ABC, PBS did an inferior job. I know that ABC’s commercials reap a good deal more cash from the network’s news operation in a month than the alms givers contribute to PBS stations in a year. But I also know that thorough reportage and editing costs no more than sloppy work. The network product is much better, and that’s not what the beggars are claiming.” (Take PBS newscasts as the population.)—George Higgins, Wall Street Journal
Yankelovich Clancy Shulman, a market research company based in Westport, Conn., asked 2,500 consumers whether they agreed or disagreed with the statement: “I feel somewhat guilty when buying non-American made products generally.” The figure was 51 percent, with a margin of error of 2 percentage points. “Something in the back of Americans’ heads is saying that they do or ought to feel guilty,” said Susan Hayward, senior vice president of Yankelovich.— Washington Post
I confess in advance that I saw only a few gusts-worth of “The Winds of War. Not the least amazing thing about the series is that so many had so many evenings free to give it. It is absolutely true that I am, metaphorically speaking, judging the roll by the caraway seed, but caraway seeds aren’t nothing. It seemed to me on brief acquaintance that the acting, to put it in a kindly way, was serviceable rather than inspired.—Charles Champlin, Los Angeles Times

14.6 Summary of Chapter Fourteen

Inductive generalizations are typically represented as arguments with a single premise, in which both the premise and the conclusion are frequency statements. When an argument satisfies the correct form condition for an inductive generalization, the premise states that a sampled portion of a population has a certain property with a certain frequency, while the conclusion says that the entire population has the same property with the same frequency. Thus, these arguments generalize from a sample to a whole. In addition, the conclusion typically allows for a margin of error; this makes the argument logically stronger by making it more probable that the conclusion is true. Large margins of error, though logically helpful, can undermine the practical value of the argument.

The total evidence condition is usually a matter of whether the sample is representative of the population as a whole. Testing for representativeness requires asking two questions. The first question is whether the sample is large enough. As a rough-and-ready rule of thumb, samples should be made up of either one or 1,000 members of the population, regardless of the size of the population itself. A sample of one is enough if the property in question is an all-or-none sort of property. Otherwise, a random sample of 1,000 is typically enough, assuming a margin of error of 3 percent is satisfactory; a larger random sample is required for a smaller margin of error, while a smaller sample requires a larger margin of error. Samples set up in this way can result in arguments with very strong inductive logic; their confidence level is .95, simply meaning that the premises support the conclusion with a .95 level of probability.

The second question is whether the sample is randomly selected. For the practical purposes of this text, this means that every member of the population has had an equal opportunity to be included in the sample, so that exactly the relevant variations of the population might be proportionately represented. If there is no obvious problem with the sample and it is the result of research by a reputable organization, then that may be enough to support the judgment that the sample is randomly selected. There can be many easy-to-detect flaws with samples, however, including grab sampling, snowball sampling, self-selected sampling, and dirty sampling.

Inductive generalizations that are logically strong may nevertheless have false premises. A special problem for such arguments is the misunderstanding of samples; the more hidden the property, the easier it is to misunderstand the sample.

Samples are sometimes used as the basis for conclusions about unsampled single members of the population, without any specific mention of an intermediate general subconclusion. These arguments are best taken as complex enthymemes, made up of an inductive generalization followed by a singular categorical argument or frequency argument.

14.7 Guidelines for Chapter Fourteen

Structure an inductive generalization, when it would be loyal to do so, so that the conclusion drops the term sampled and adds a margin of error.
In the premise of an inductive generalization, translate stylistic variations into the logical constant sampled.
When the principle of loyalty allows, paraphrase inductive generalizations so as to include a non-zero margin of error in the conclusion.
In considering whether an inductive generalization has satisfied the total evidence condition, first ask, Is the sample large enough?
If the property is likely to be all-or-none, then a sample of one is typically enough. It is almost certainly not an all-or-none property if there has been an effort to scientifically construct the sample.
For properties that are not all-or-none, if the margin of error increases appropriately as the sample size decreases, then the logical strength of the argument remains steady.
When the population is large, variation in population size has no bearing on the size of the random sample that is needed, although it may have a bearing on how easy it is to get a random sample.
Judge as very strong the logic of any inductive generalization that renders its conclusion .95 probable.
Do not judge an inductive generalization to be strong unless its sample is randomly selected—that is, unless the sample includes the relevant variations in the appropriate frequency. Remember that not all variations are relevant.
Be alert for ways in which an argument may fail to include a relevant variation in its sample. Typically, arguments that depend on grab sampling, snowball sampling, self-selected sampling, or dirty sampling do not have randomly selected samples and are thus logically very weak (and, thus, unsound).
Be especially alert for ways in which the sample might have been misunderstood, thus producing a false premise. The more hidden the property, the greater the opportunity for misunderstanding.
When an argument moves from a sample to a specific instance, clarify and evaluate it as an inductive generalization followed by a singular categorical argument or frequency argument.

14.8 Glossary for Chapter Fourteen

Confidence level —the logical strength of the argument; the frequency with which the conclusion would be true if the premise(s) were true.

Dirty sampling —the contamination—usually unintentional—of a sample by the sampling process itself. This is a failure of randomness. In a randomly selected sample, exactly the relevant variations of the population are proportionately represented. Introducing contamination is introducing a relevant variation that is not in the population.

Fallacy of hasty generalization —the mistake of arguing from a sample that is not representative—that is not large enough or randomly selected. It is normally more illuminating if you avoid this term and focus your evaluation on the more specific mistakes made by the argument.

Grab sampling —the process of including in your sample whatever members of the population happen to come your way. This is a failure of randomness.

Inductive generalization —argument that draws general conclusions about an entire population from samples taken of members of the population. Form is:

Margin of error —in the conclusion to an inductive generalization, the range of frequencies within which the property is stated to occur. Also called the confidence interval.

Misunderstood sample —when the method used for collecting information about the sample is not entirely reliable it results in a misunderstanding of the sample’s properties, rendering the premise false. The more hidden the property (people’s attitudes, for example, are easily hidden), the more likely the misunderstanding.

Random selection —the process of selecting a sample such that every member of the population has had an equal opportunity to be included, so that exactly the relevant variations of the population might be proportionately represented.

Self-selected sampling —when members of the population decide for themselves whether to be included in the sample. This is a failure of randomness.

Snowball sampling —the process of adding new members to the sample on the basis of their close relationship with those already included (thus gathering members in the same way that a snowball gathers snow as it rolls along). This is a failure of randomness.

Stratification —the construction of a random sample, for practical purposes, by identifying groups within the population that tend to be relatively uniform and including strata, or groups, of the sample in numbers that proportionally represent their membership in the entire population.

This is not the only form, just the most common. There are also, for example, comparative inductive generalizations ; which may be clarified as follows: 1. Sampled F has H n more (or less) than sampled G . ∴ C . F has H n (+ l - m ) more (or less) than G ↵
As statisticians would put it, the overlap in the margins of error means that the difference in the two results is not statistically significant ↵
A property that is normally an all-or-none property is, nevertheless, not necessarily such a property. If you have reason to think that the soup has not been stirred, or that this copy of the newspaper is a dummy, then a sample of one is not sufficient. ↵

Argument that draws general conclusions about an entire population from samples taken of members of the population. Form is: 1. n of sampled F are G. (Where n is any frequency, including 0 and 1.) ∴ C. n (1 or 2 m ) of F are G.

In the conclusion to an inductive generalization, the range of frequencies within which the property is stated to occur. Also called the confidence interval.

The mistake of arguing from a sample that is not representative—that is not large enough or randomly selected. It is normally more illuminating if you avoid this term and focus your evaluation on the more specific mistakes made by the argument.

The logical strength of the argument; the frequency with which the conclusion would be true if the premise(s) were true.

The process of selecting a sample such that every member of the population has had an equal opportunity to be included, so that exactly the relevant variations of the population might be proportionately represented.

The construction of a random sample, for practical purposes, by identifying groups within the population that tend to be relatively uniform and including strata, or groups, of the sample in numbers that proportionally represent their membership in the entire population.

The process of including in your sample whatever members of the population happen to come your way. This is a failure of randomness.

The process of adding new members to the sample on the basis of their close relationship with those already included (thus gathering members in the same way that a snowball gathers snow as it rolls along). This is a failure of randomness.

When members of the population decide for themselves whether to be included in the sample. This is a failure of randomness.

The contamination—usually unintentional—of a sample by the sampling process itself. This is a failure of randomness. In a randomly selected sample, exactly the relevant variations of the population are proportionately represented. Introducing contamination is introducing a relevant variation that is not in the population.

When the method used for collecting information about the sample is not entirely reliable it results in a misunderstanding of the sample’s properties, rendering the premise false. The more hidden the property (people’s attitudes, for example, are easily hidden), the more likely the misunderstanding.

A Guide to Good Reasoning: Cultivating Intellectual Virtues Copyright © 2020 by David Carl Wilson is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Homepage » Logic » Arguments Index » Deductive and Inductive Arguments

Deductive and Inductive Arguments

Abstract : A deductive argument's premises provide conclusive evidence for the truth of its conclusion. An inductive argument's premises provide probable evidence for the truth of its conclusion. The difference between deductive and inductive arguments does not specifically depend on the specificity or generality of the composite statements. Both kinds of arguments are characterized and distinguished with examples and exercises.

Deductive Arguments Defined
Some Types of Deductive Arguments
Inductive Arguments Defined
Some Types of Inductive Arguments
Specificity and Generality of Statements Do Not Always Distinguish Deduction From Induction

How to Distinguish Inductive Arguments from Deductive Arguments:

Additional Examples Distinguishing Deduction and Induction

The Difference between Deduction and Induction:

“When an argument is such that the truth of the premises guarantees the truth of the conclusion, we shall say that it is deductively valid. When an argument is not deductively valid but nevertheless the premises provide good evidence for the conclusion, the argument is said to be inductively strong.” [2]

Deductive Arguments Defined:

All men are mortal. Socrates is a man. Therefore, Socrates is mortal.

All B is [in] C . All A is [in] B . ∴ All A is [in] C .

If the premises are true and they necessitate the truth of the conclusion, then the argument is said to be deductively valid and sound . In such a case, it is impossible for the conclusion to be logically inconsistent with the premises.

Some Examples of Types of Deductive Arguments:

”Peter is John's brother, so John must be Peter's brother.” The argument is deductive since it relies on the lexical definition of “brother.” (Note this trivial deductive argument has no general statements.)

“Mystery is delightful, but unscientific, (2) since it depends upon ignorance.“ [4]

c. “Grant that the phenomena of intelligence conform to laws; grant that the evolution of intelligence in a child also conforms to laws; and it follows inevitably that education cannot be rightly guided without a knowledge of those laws.” [5]

No druggist is a chemist. That's because all apothecaries are chemists.

All [apothecaries] are [chemists].

→ {No [apothecaries] are [druggists]}

No [druggists] are [chemists].

E.g. , “Since a shell weighing 64 lbs leaves a gun with a velocity of 3,000 feet per second, and arrives at a target with a striking velocity of 500 feet per second, 11,250 BTU of heat resistance is generated.” [6]

Logical inferences : arguments which can be described by symbolic notation in order to simplify the relationships among the structures (rather being evaluated from the meaning of the statements themselves). E.g. , “If you w ork hard, then you will s ucceed, and if you s ucceed, then you will be h appy; therefore, if you w ork hard, you will be h appy.” Given w → s → h , it follows w → h (These types of inferences follow from the truths of logic. The logic rule used here is called a hypothetical syllogism.)

Inductive Arguments Defined:

Inductive arguments can range in probability from very low to very high, but always less than 100%. The probability of the conclusion drawn from an inductive argument is only an estimate and usually not known exactly. [7] (Note that the mathematical calculations in statistical reasoning are deductive even though the conclusions themselves are only probable. In other words, in statistics, the probability expressed in the conclusion follows from the premises with mathematical necessity.)
Often (but not always!) induction is the sort of inference which attempts to reach a conclusion concerning all the members of a class or group on the basis of the observations of only some of them. So to put it another way, the conclusion of a very strong inductive argument with true premises is improbably false.

“I've seen many persons with creased earlobes who have had heart attacks, so I conclude that (all) persons who have creased earlobes are prone to have heart attacks.” [8]

Some Examples of Types of Inductive Arguments:

Modal Verbs and Probability Indicators:

Extrapolation:.

“A systematic evaluation of genotoxic responses will allow us to determine how genotoxic effects in rodents extrapolate to similar effects in humans. Research has already indicated that human cells may be more capable than rodent cells of repairing at least some DNA lesions, implying that human cells may be less sensitive to genotoxic agents.” [10]

“Since past experience indicates that irrigation is necessary for sustained production, the cost of a commercial grove with irrigation facilities would probably be at least $200.00 per acre higher than the official estimate.” [11]

One bird species with one color-form in the same population has been shown to be relatively stable over time, so all bird species with one color-form in that same population will remain relatively stable over time, as well.

“According to a Jenkins Group survey, 42% of college graduates will never read another book. Since most people read bestsellers printed in the past 10 years, it follows that virtually no one is reading the classics.” [13]

”[The reason] as to why productivity has slumped since 2004 is a simple one. That year coincided with the creation of Facebook .” [14]

“I share … [a] disrespect for religious certitude, which is a simulacrum of faith; but suggest that scientific certitude is barely less lethal. Just as we do not judge the value of science by nuclear weapons, pollution and junk food, we should not judge religion by its abuses.” [15]

Specificity and Generality of Statements Do Not Always Distinguish Deductive Arguments from Inductive Arguments:

All organisms have chromosomes. [ This fruit fly is an organism.] ∴ This fruit fly has chromosomes.

A red-eyed fruit fly has large chromosomes. A white-eyed fruit fly has large chromosomes. A Hawaiian fruit fly has large chromosomes. ∴ All fruit flies have large chromosomes.

Only Plato and Aristotle were great Greek philosophers. Plato and Aristotle lived in Athens. ∴ All the great Greek philosophers lived in Athens.

Each senator was present at today's session. ∴ All senators were present at today's session.

Entities E 1 , E 2 , and E 3 all have property p . Entities E 1 , E 2 , and E 3 are the only members of class M . ∴ All members of class M have property p .

All the great Greek philosophers wrote treatises on science. All philosophers named Aristotle wrote treatises on science. ∴ Aristotle was a great Greek philosopher. [17]

The whale is a mammal. [as in an encyclopedia entry]
All novelists of Waverly named Sir Walter Scott are historical writers. [a definite description]
All present kings of France are bald. [a non-existent entity]
All ideal gases are perfectly elastic. [a theoretical entity or nonobservable entity]

Begging the Question:

George is a man. George is 100 years old. George has arthritis. ∴ George will not run a four-minute mile. [19]

For example adding the information that that George has a sprained ankle, a broken leg, and a heart condition makes it even less likely that George can run a 4 minute mile.
However, when we add the premise that George is paraplegic, then the argument is transformed into a deductive argument because now the conclusion follows with certainty by the meanings of the words used in the statements.

Two performers in the Kronos Quartet play violin, one plays viola and another plays cello. ∴ The Kronos Quartet is composed of performers who all play stringed instruments.

In valid deductive arguments, if the premises are true, then the truth of the conclusion follows with certainty.

“If we hate a person, we hate something in him that is part of ourselves. What isn't part of ourselves doesn't disturb us.” [20]

What isn't part of ourselves doesn't disturb us. ∴ If we hate a person, we hate something in him that is part of ourselves.

All things disturbing us are things part of ourselves. ∴ Our hating a person is hating something in him which is part of ourselves.

All [ things disturbing us ] are [things part of ourselves]. → {[Our hating a person] is [a thing that disturbs us ].} ∴ [Our hating a person] is [hating a thing part of ourselves].

Additional Examples Distinguishing Deduction and Induction:

All throughout history people repeat the same mistakes, so we can conclude that similar mistakes will be made in the future. Answer Inductive Argument —The conclusion does not follow with absolute certainty. The reasoning assumes that the future will be in some sense like the past.
The whale is a mammal, so all killer whales are mammals. Answer Deductive Argument — With the implicit premise that killer whales are whales, the conclusion follows with absolute certainty. In this example, the reasoning does proceed from general to less general, but the first general statement can be misleading to some persons.
All killer whales are mammals, so the whale is a mammal. Answer Inductive Argument — As the argument stands, the conclusion is only probable. Notice that the reasoning is from part to whole even though the argument “appears” to be reasoned from general to specific. Even if it is assumed that all persons know whales are necessarily mammals, the reasoning in this argument is that the reason whales are mammals is due to one of its subclasses (killer whales) being mammals. This reason, considered by itself, is insufficient to prove the truth of the conclusion.
“Because of our preoccupation with the present moment and the latest discovery, we do not read the great books of the past. Because we do not do this sort of reading, and do not think it is important, we do not bother about trying to learn to read difficult books. As a result, we do not learn to read well at all.” [21] Answer Inductive Argument and/or Explanation — Depending on the context of the passage, it is most likely to be an explanation as to why many persons do not read well rather than an argument proving why we do not read well. If it is evaluated as an argument, then it would be inductive, since it is possible for someone who has already learned to read well to be preoccupied at the present moment and that is why that person does not now read great or difficult works.

All persons who only look upon friends for profit are people who do not seek friendship without some ulterior motive. ∴ They seek only to profit from friends and don't look solely for friendship-in-itself ( i.e. , a friendship without an ulterior motive.) “Africans are notoriously religious, and each people has it own religious system with a set of beliefs and practices. Religion permeates into all the departments of life so fully, that it is not easy or possible always to isolate it. A study of these religious systems, is, therefore, ultimately a study of the peoples themselves in all the complexities of other traditional and modern life.” [23] Answer Inductive Argument — The argument is a strong inductive argument since a premise indicates it is not always easy or possible to study each people apart from their religion, suggesting that in some cases studying some people without considering their religion might be possible. So the conclusion does not follow with absolute certainty. It might be surprising to note that had the conclusion substituted the phrase “almost always” for “ultimately”, the argument would have been deductive. That is, the argument would be comparable to the following simplification: Most African people's religious beliefs are integrated into their lives. A study of African religions involves studying how most African peoples live.

“Political change is a process that consolidates privilege, which further entrenches the oligarchic order. Thus the every widening economic gap — inevitably, a cultural and political gap—between the rich and the poor.” [24] Answer Deductive Argument — The conclusion is claimed to follow with complete certainty as indicated by the adverb “inevitably” in the conclusion of the argument.
“[A]nxiety sufferers … may respond to their feelings by leaving nothing to chance. At work, they appear polished and prepared when giving a presentation because they consider every question that could be posed by colleagues beforehand and memorize possible answer in the days leading up to a meeting.” [25] Answer Inductive Argument — Since the first sentence, the conclusion of this argument, includes the modal verb “may” the conclusion is not claimed to follow with certainty. Hence, this argument is inductive since the conclusion is claimed to follow with some degree of probability.
”The exegete needs to possess not only scholarly training but also the continual deepening of his own meditative experience, so that he will not introduce mistaken interpretations into the text. Thus he is required not only to read the subject deeply enough to penetrate its key themes, but also to meditate in order to have the necessary mental purity and ‘wisdom eye&rsqquo; to carry out the work.” [26] Answer Deductive Argument —The meaning of the premise phrasing “scholarly training” and “deepening meditative experience” respectively imply the meaning of the conclusion phrasings ”reading deeply” and “meditate in order to have the necessary mental purity …” So the conclusion follows from the premise by the meaning of the words used.
“One thing upon which Africana scholars and intellectuals largely agree is that the criteria used to define what is and what is not philosophy in the world today are unfairly biased by and for ‘philosophy’ as presently construed by Western culture. There may have to be some common ground if the work ‘philosophy’ is to continue to have cross-cultural significance. But Africa, in particular, has not received just consideration in that regard … In so many respects, it seems, Africa's cultures have not benefited from the kinds of exhaustive and empathetic scholarship that are being lavished upon other parts of the world. The oral literature of the African continent, therefore, has not even begun to receive the attention it merits.” [27] Answer Inductive Argument — Since most Africana scholars agree Western culture's definition of philosophy is biased, this assumed bias is reported to be the reason the Western definition has little common ground with the oral literature of Africa and the reason African philosophy is underrepresented in the world. This argument is inductive as its claim is based on a consensus rather than universal accord. Note that this argument is not an ad populum fallacy (an inappropriate appeal to the people) nor an ad verecundiam fallacy (an appeal to an irrrelevant authority) since Africana scholars and intellectuals are proper authorities as to the nature of African thought.

Ngram graph showing historical frequency of deductive argument and inductive argument in Google books form 1800 to 2008

Deduction and Induction Notes

1. Richard Whately pointed out in 1831 that induction can be stated as a syllogism with a suppressed universal major premise which is substantially “what belongs to the individual or individuals we have examined, belongs to the whole class under which they come.” [Richard Whately, Elements of Logic (London: B. Fellowes, 1831), 230.] This influential text led many early logicians ( e.g. , John Stuart Mill) to think mistakenly that inductive logic can be somehow transformed into demonstrative reasoning. Following, George Henrik von Wright's A Treatise on Induction and Probability (1951 Abingdon, Oxon: Routledge, 2003. doi: 10.4324/9781315823157 ), logicians have abandoned this program [ C.f. , 29-30].

There is some controversy in the recent informal logic movement as to whether conductive, abductive, analogical, plausible, and other arguments can be classified as either inductive or deductive. Conductive, abductive and analogical arguments in this course are interpreted and reconstructed as inductive arguments.

A conductive argument is a complex argument which provides premises which separately provide evidence for a conclusion — each is independently relevant to the conclusion. Conductive arguments can also provide evidence for and against a conclusion (as in evaluations or decision).

Abductive argument is a process of selecting hypotheses which best explain a state of affairs very much like inference to the best explanation.

An analogical argument specifies that events or entities alike in several respects are probably alike in other respects as well. See e.g. Yun Xie, “ Conductive Argument as a Mode of Strategic Maneuvering ,” Informal Logic 37 no. 1 (January, 2017), 2-22. doi: 10.22329/il.v37i1.4696 And Bruce N. Waller, “ Classifying and Analyzing Analogies ” Informal Logic 21 no. 3 (Fall 2001), 199-218. 10.22329/il.v21i3.2246 ↩

2. Bryan Skyrms, Choice and Chance: An Introduction to Inductive Logic (Dickenson, 1975), 6-7.

Some logicians argue that all arguments are exclusively either deductive or inductive, and there are no other kinds. Also, they claim deductive arguments can only be evaluated by deductive standards and inductive arguments can only be evaluated by inductive standards. [ E.g. , George Bowles, “The Deductive/Inductive Distinction,” Informal Logic 16 no. 3 (Fall, 1994), 160. doi: 10.22329/il.v16i3.2455 ]

Stephen Barker argues:

“Our definition of deduction must refer to what the speaker is claiming, if it is to allow us to distinguish between invalid deductions and nondeductions.”

[S.F. Barker, “Must Every Inference be Either Deductive or Inductive?,” in Philosophy in America ed. Max Black (1964 London: Routledge, 2013), 62.]

On the one hand, for monotonic reasoning, Barker's definition makes the tail wag the dog since on this view the distinction between the two kinds of arguments depends upon the arbitrary psychological factor of what type of argument someone declares it to be rather than the nature or character of the argument itself. On Barker's view (and many current textbook views), the speaker's claim determines whether an argument is deductive or inductive regardless of the structure of the argument itself.

Barker explains the distinction from a dialogical point of view:

“Suppose someone argues, ‘All vegetarians are teetotallers, and he's a teetotaller, so I think he's a vegetarian.’ Is this inference a definitely illegitimate deduction, or is it an induction which may possibly be logically legitimate? We cannot decide without considering whether the speaker is claiming that his conclusion is strictly guaranteed by the premises (in which case, the inference is a fallacious deduction) or whether he is merely claiming that the premises supply real reason for believing the conclusion (in which case, the inference is an induction which in an appropriate context might be legitimate).” [Barker, 66.]

On Barker's view, an invalid deduction cannot be considered a weak induction since, for him, deduction and induction are exclusive forms of argumentation. This is a popular view, but we do not follow this view in these notes. Trudy Govier points out:

“If arguers' intentions are to provide the basis for a distinction between deductive and inductive arguments which will be anything like the traditional one, those arguers will have to formulate their intentions with a knowledge of the difference between logical and empirical connection, and the distinction between considerations of truth and those of validity.”

[Trudy Govier, “ More on Deductive and Inductive Arguments ,” Informal Logic (formerly Informal Logic Newsletter ) 2 no. 3 (March, 1979), 8. doi: 10.22329/il.v2i3.2824 ]

This point is obvious for monotonic reasoning where arguments are evaluated independently of claims (1) by the person who espouses them or when (2) arguments are evaluated in terms of the principle of charity . Even for dialogical reasoning, a speaker's intention should not determine the distinction between inductive and inductive arguments, for few speakers are informed of the epistemological differences to begin with. ↩

3. “Intentional account” named by Robert Wachbrit, “ A Note on the Difference Between Deduction and Induction ,” Philosophy & Rhetoric 29 no. 2 (1996), 168. doi: 10.2307/40237896 (doi link not activated 2022.06.28) ↩

4. Bertrand Russell, The Analysis of Mind (London: George Allen & Unwin, 1921), 40. ↩

5. Herbert Spencer, Education: Intellectual, Moral and Physical (New York: D. Appleton, 1860), 45-46. ↩

6. O.B. Goldman, “ Heat Engineering ,” The International Steam Engineer 37 no. 2(February 1920), 96. ↩

7. Arguments in statistics and probability theory are mathematical idealizations and are considered deductive inferences since their probable conclusions are logically entailed by their probable premises by means of a “rule-based definitions.”

Consequently, even though the premises and conclusion of these arguments are only probable, the probabilistic conclusion necessarily follows from the truth of the probabilistic premises. The inference itself is claimed to be certain given the truth of the premises.

In a valid deductive argument the conclusion must be true, if the premises are true. The proper description of the truth value of the conclusion of a valid statistical argument is that the statistical result is true, if the premises are true. The truth of the probability value established in the conclusion is certain given the truth of the data provided in the premises. ↩

8. This inductive argument is suggested by this study: Aris P. Agouridis, Moses S. Elisaf, Devaki R. Nair, and Dimitri P. Mikhailidis, “ Ear Lobe Crease: A Marker of Coronary Artery Disease? ” Archives of Medical Science 11 no. 6 (December 10, 2015) 1145-1155. doi: 10.5114/aoms.2015.56340> ↩

9. Friedrich Schlegel, Lectures on the History of Literature: Ancient and Modern trans. Henry G. Bohn (London: George Bell & Sons, 1880), 34. ↩

10. R. Schoeny and W. Farland, “ Determination of Relative Rodent-Human Interspecies Sensitivities to Chemical Carcinogens/Mutagens, ” Research to Improve Health Risk Assessments (Washington, D.C.: U.S. Environmental Protection Agency, 1990), Appendix D, 44. ↩

11. Foreign Agriculture Circular (Washington D.C.: U.S. Department of Agriculture, 5 no. 64 (November, 1964), 4. ↩

12. This type of induction describes the most common variety: it's often called “induction by incomplete enumeration.” ↩

13. John Wesley, “ 10 Ways to Improve Your Mind by Reading the Classics ,” Pick the Brain: Grow Yourself (June 20, 2007). ↩

14. Adapted from Nikko Schaff, “Letters: Let the Inventors Speak,” Economist 460 no. 8820 (January 26, 2013), 16. ↩

16. Historically, from the time of Aristotle, the distinction between deduction and induction, more or less, has been described as:

“[I]nduction is a progression from singulars to universals … and induction is more calculated to persuade, is clearer, and according to sense more known, and common to many things.” [Aristotle, Top. I.xii 105a12-13;16-19 (trans. Owen)

“Induction, then, is that operation of the mind, by which we infer that what we know to be true in a particular case or cases, will be true in all cases which resemble the former in certain assignable respects. In other words, Induction is the process by which we conclude that what is true of certain individuals of a class is true of the whole class, or that what is true at certain times will be true in similar circumstances at all times.” [John Stuart Mill, A System of Logic 2 vols.(London: Longmans, Green, Reader, and Dyer,) I:333.]

“[D]eduction consists in passing from more general to less general truths; induction is the contrary process from less to more general truths.” [W. Stanley Jevons, The Principles of Science 2nd ed. rev. (1887 London: Macmillan, 1913), 11.]

This view remains a popular view and does distinguish many arguments correctly. However, since this characterization is not true in all instances of these arguments, this distinction is no longer considered correct in the discipline of logic.

William Whewell was perhaps the earliest philosopher to register a correction to the view that induction can be defined as a process of reasoning from specific statements to a generalization. Throughout his writings he explains that induction requires more than simply generalizing from an enumeration of facts. He suggests as early as 1831 that the facts must be brought together by the recognition of a new generality of the relationship among the facts by applying that general relation to each of the facts. See. esp. William Whewell, The Mechanical Euclid (Cambridge: J. and J.J. Deighton, 1837), 173-175; The Philosophy of the Inductive Sciences , vol. 2 (London: J.W. Parker and Sons, 1840), 214; On the Philosophy of Discovery (London: John W. Parker and Son, 1860), 254. ↩

17. Notice that if this argument were to be taken as a syllogism (which will be studied later in the course), it would be considered an invalid deductive argument. A valid deductive argument has its conclusion follow with necessity; when the conclusion does not logically follow as in the “great Greek philosophers” example, there still is some small bit of evidence for the truth of the conclusion, so the argument could be evaluated as an extremely weak inductive argument.

No matter what class names ( i.e. no matter what subjects and predicates) are substituted into the form or grammatical structure of this argument (assuming the statements themselves are not tautological in some sense), it could never be a valid deductive argument — even when all the statements in it happen to be true. ↩

18. P.F. Strawson distinguishes the particular and the general in this manner:

“[W]hen we refer to general things, we abstract from their actual distribution and limits, if they have any, as we cannot do when we refer to particulars. Hence, with general things, meaning suffices to determine reference. And with this is connected the tendency, on the whole dominant, to ascribe superior reality to particular things. Meaning is not enough, in their case, to determine the reference of their designations; the extra, contextual element is essential. … So general things may have instances, while particular things may not.”

P.F. Strawson, “ Particular and General ,” Proceedings of the Aristotelian Society New Series 54 no. 1 (1953-1954), 260. doi: 10.1093/aristotelian/54.1.233 Also by JStor (free access by registration). ↩

19. Bryan Skyrms, Choice and Chance: An Introduction to Inductive Logic (Dickenson, 1975), 7. ↩

20. Adapted from Hermann Hesse, Demian (Berlin: S. Fischer, 1925), 157. ↩

21. Mortimer J. Adler, How to Read a Book (New York: Simon and Schuster: 1940), 89. ↩

22. Marcus Tullius Cicero, Old Age in Letters of Marcus Tullius Cicero with his Treatises on Friendship and Old Age and Letters of Gaius Plinius Caecilius Secundus , trans. E.E. Shuckburgh and William Melmoth, Harvard Classics, vol. 9 (P.F. Collier & Son, 1909), 35. ↩

23. John S. Mbiti, African Religions & Philosophy (Oxford: Heinemann, 1969), 1. ↩

24. Ferdinand E. Marcos, The Democratic Revolution in the Philippines (Englewood Cliffs, NJ: Prentice-Hall, 1974), 93. Also, Ferdinand E. Marcos, Toward the New Society: Essays on Aspects of Philippine Development (Philippines: National Media, 1974), 7. ↩

25. Francine Russo, “The Personality Trait ‘Intolerance of Uncertainty’ Causes Anguish During COVID,” Scientific American Mind 33 no. 3 (May-June 2022), 14. Also, here: Francine Russo, “ The Personality Trait ‘Intolerance of Uncertainty’ … ” Scientific American (accessed June 25, 2022). ↩

26. Charles Muller, “ A Korean Contribution to the Zen Canon; The Oga Hae Seorui (Commentaries of Five Masters on the Diamond Sūtra) ,” in Zen Classics: Formative Text in the History of Zen Buddhism eds. Steven Heine and Dale S. Wright (Oxford: Oxford University Press, 2006), 54. ↩

27. Barry Hallen, Short History of African Philosophy 2nd.ed. (Bloomington: Indiana University, 2009), 21. ↩

Readings on Induction and Deduction

S.F. Barker, “Must Every Inference be Either Deductive or Inductive?,” in Philosophy in America ed. Max Black (1964 London: Routledge, 2013), 62. doi: 10.4324/9781315830636

George Bowles, “ The Deductive/Inductive Distinction ,” Informal Logic 16, no. 3 (Fall 1994), 159-184. doi: 10.22329/il.v16i3.2455

Trudy Govier, “ More on Deductive and Inductive Arguments ,” Informal Logic (formerly Informal Logic Newsletter ) 2 no. 3 (March, 1979), 7-8. doi: 10.22329/il.v2i3.2824

David Hitchcock, “ Deduction, Induction and Conduction ,” 3 no. 2 Informal Logic (formerly Informal Logic Newsletter ) (January, 1980), 7-15. doi: 10.22329/il.v3i2.2786

IEP Staff, “ Deduction and Induction ,” The Internet Encyclopedia of Philosophy

P.F. Strawson, “ Particular and General ,” Proceedings of the Aristotelian Society New Series 54 no. 1 (1953-1954), 233-260. Also by JStor (free access by registration). doi: 10.1093/aristotelian/54.1.233

Robert Wachbrit, “ A Note on the Difference Between Deduction and Induction ,” Philosophy & Rhetoric 29 no. 2 (1996), 168-178. doi: 10.2307/40237896 (doi link not activated 2022.06.25) JStor (free with registration)

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Inductive Argument Examples

Inductive argument , or inductive reasoning, is a type of logical thought pattern that moves from the specific to the general. This is the opposite of deductive reasoning, which begins with a general statement and moves to a specific conclusion.

Example of Inductive Reasoning

Joe wore a blue shirt yesterday. Joe's shirt today is blue. Joe will wear a blue shirt tomorrow as well.

Notice how the inductive argument begins with something specific that you have observed. It moves to a drawing a more general conclusion based on what you have observed in a specific instance (or in this case, on two specific days). Just because you can draw a conclusion using inductive argument , it doesn't mean that the conclusion is true or valid. Joe might wear a green shirt tomorrow-or a shirt in any color he wants.

Examples of Inductive Argument

1. The first three Skittles that I dumped out of the bag were purple. All of the Skittles in this bag must be purple. 2. Mrs. Crown has given a quiz on the first two Fridays of the school year. She will probably give a quiz every Friday. 3. Sara went to the library, and then Ann went. Sara went to sharpen her pencil, and then Ann went. Ann is copying Sara today. 4. My mom packed a red apple in my lunch on Monday. She packed an orange on Tuesday, she packed a red apple on Wednesday. Today is Thursday, and I think she will pack an orange. 5. My sister likes cats. Jeff's sister likes cats. Marks's sister likes cats. All girls must like cats. 6. Every morning at the beach, it has rained. I think it will rain again this morning. 7. The sun always comes up and shines in the windows on the back of our house. The sun always sets and shines in the windows on the front of our house. I think the sun will shine through the windows on the front of the house when it sets tonight.

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Analysis of inductive argument

This is a method of reasoning called induction. This method generally entails the use of inference method, collective principals, and law about a particular issue as well as argument. Moreover, in the case of opinion poll, it is the representation of a scientific research to rate the views of a given group. The group concerns are the union trade members, or those who participate in voting. In the American context, there are two main political parties namely the Democratic Party and the Republican Party (Moore, 2012, p.112).

For a particular organization to take a poll survey, it is important for the organization to formulate the questionnaire that will act as a guideline for the research. These usually enhance the accuracy of the research. Some of the questions that should feature include; is Election Day open on polling? Are the options available for the voter to choose? What parties can one vote? Who is the most popular presidential candidate?

The sample size that was used during the research constituted of 1054 people. The sampled individuals were mainly adults drawn from all parts of the nation. It is pertinent to note that this sample size was large enough to avoid hastiness despite having exceeded the recommend number, which is always 1000. This is a considerable number in that with the occurrence of the error notwithstanding, the target will be met Govier, 2009, p130). It is imperative that the sample brings into existence the landline and cell phones that came into operation. This work successfully came up by the use of random selection using a computer. This random selection came out of almost 69000 exchanges of residents across the country to ensure all regions of the country are equally represented. The use of a computer alleviated biasness whilst accounting for correct proportionality. It is pertinent to note that after each random exchange, the digits add up to make a full number of a telephone, therefore, enhancing accessibility to the unlisted and listed numbers. This enabled the cell phone random dial numbers reach the respondents. It is noteworthy that those who admit supporting the democrats are over sampled in the same poll. The sample came into another stage, where the Democratic Party literally compared to republican and other parties, thus bringing about the difference.

Biasing is another factor that can accrue whenever there is an election. Thus, United States is not exception since Obama’s success did encounter biasing. Bias result mostly in the media since it is the widest form of communication. In most cases, media frequently favors the one who is on powerful and president. Obamas’ team has the ability to put its pressure on the media. This is evident to the BBC news on recent period during the countries’ opinion poll voting. News did rotate around the good performance of the president. In 2011, September, more than 15000 voters did cast their opinion poll, and surprisingly 34% to 36% came about in favor of the Republican Party. Nevertheless, against much expectation, Washington Post/ABC did show that the data analysis came in favor of Obama by 5% surge.

The target population is always adults who has 18 years of age and who has actually had a birthday recently. In the household, this is the method pollster uses to select voters. Every adult individual has an opportunity to take participation in voting. There are two types of Gallup poll; these include traditionally stand-alone poll, and a night’s interview (Heit & Feeney, 2007). The Gallup poll is usually 1000 adults with an error margin of +or -4 points in percentage. However, the daily tracking plans has open Gallup poll analyst to use a considerable amount of groups. Fortunately, accuracy of the estimation does derive from a larger sample size that is marginal.

On scale representation, the rating is labeled 1 to 10 such that 1 denotes the weakest while 10 is the strongest. Polls can be rated as 5 because polls are neither weak nor strong. Poll depends with the nature of persons and the authority that the president, or the power holder posses unlike the civilians. If the government holds powerful opposition, the poll will be rated 5 (Chakraborti, 2006 p242). However, if atoll, opposition is weak, then biasing and other unfair factors will penetrate through and so the stability of the country will be poor and weak almost to one.

In conclusion, strong poll represents the voice of the citizens in a given country to facilitate change. In addition, if fairness undergoes consideration, people do vote and stand by their rights. If one passes the poll, he/she has respect of the natives of the country. Strong poll surpasses the set level by almost 10% and so this shows that when people speaks, so has spoken. Nevertheless, a weak poll is weak since the majority of its vote comes from a less number or portion of the entire country. This is a clear indication that where poll do not have fans or its not favorite party to peoples’ decision on getting change, or persuasive power of the competing party (Heit, 2007, 321). Weak poll entails how the party is as well weak, and a strong poll, entails how the party in favor is strong. Therefore, strong is always strong and weak is always weak.

Chakraborti, C. (2006). Logic: Informal, Symbolic and Inductive . PHI Learning Pvt.

Govier, T. (2009). A Practical Study of Argument. New Yok, NY: Cengage Learning.

Heit, E., & Feeney, A. (2007). Inductive Reasoning: Experimental, Developmental, and Computational Approaches . Cambridge University Press.

Moore, K. (2012). Developing Critical Thinking . Armstedam: Kendall/Hunt.

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“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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Inductive Essay Examples
Inductive Essay Examples. 20 samples. 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.
Inductive Reasoning
Inductive reasoning generalizations can vary from weak to strong, depending on the number and quality of observations and arguments used. Inductive generalization. Inductive generalizations use observations about a sample to come to a conclusion about the population it came from. Inductive generalizations are also called induction by enumeration.
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15 Inductive Reasoning Examples. Inductive reasoning involves using patterns from small datasets to come up with broader generalizations. For example, it is used in opinion polling when you poll 1,000 people and use that data to come up with an estimate of broader public opinion. Typically, inductive reasoning moves from the specific to the ...
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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…
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How to Write an Inductive Essay
Induction and deduction are opposite forms of reasoning. Deduction is a type of formal logic in which you can arrive at a conclusion based on the truth of generalization. For instance, if all llamas are mammals, and Edgar is a llama, then you may deduce that Edgar is a mammal. Induction takes the opposite approach, ...
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Revised on June 22, 2023. The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. In other words, inductive reasoning moves from specific observations to broad generalizations. Deductive reasoning works the other way around.
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An inductive essay is a type of writing that aims to persuade the reader to accept a conclusion based on the presentation of evidence or examples. This type of essay is often used in academic writing to explore a particular topic, to draw conclusions about it, and to convince the reader that those conclusions are valid.
Free Inductive Essay Examples. Best Topics, Titles GradesFixer
Inductive essay examples are useful for providing a clear understanding of how to construct an inductive argument. They can help students and writers learn how to effectively gather evidence, draw conclusions, and present logical reasoning in their essays. By studying examples, individuals can grasp the structure and flow of an inductive essay ...
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
Inductive and Deductive Reasoning
Inductive reasoning can often be hidden inside a deductive argument. That is, a generalization reached through inductive reasoning can be turned around and used as a starting "truth" a deductive argument. For instance, Most Labrador retrievers are friendly. Kimber is a Labrador retriever. Therefore, Kimber is friendly.
PPTX Writing the Inductive Essay
Writing the Inductive Essay. Going from examples to conclusions. Inductive Writing. Looks at specific instances and culminates in the conclusion. Used for controversial topics to maintain reader attention. A complete thesis in the beginning can deter readers who disagree. You can build your argument slowly.
Inductive Reasoning (Definition + Examples)
Inductive reasoning is a method where specific observations or experiences are used to reach a broader, general conclusion. In contrast to deductive reasoning, which starts with a general statement and examines the possibilities to reach a specific conclusion, inductive reasoning begins with specific examples and tries to form a general rule.
Inductive & Deductive Reasoning
In the context of this deductive reasoning essay, an argument from analogy is one of the examples under deductive reasoning. The rule underlying this module is that in the case where P and Q are similar and have properties a, b, and c, object P has an extra property, "x.". Therefore, Q will automatically have the same extra property, "x ...
Inductive vs Deductive Reasoning
Revised on 10 October 2022. The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. Inductive reasoning moves from specific observations to broad generalisations, and deductive reasoning the other way around.
Chapter Fourteen: Inductive Generalization
If an inductive generalization is to be logically successful, it—like all other inductive arguments—must satisfy both the correct form condition and the total evidence condition. ... In deductively valid arguments, for example, the conclusion would be true every time you considered the premises; thus, they confer a probability of 1.00 on ...
Inductive Arguments
"An inductive argument can be affected by acquiring new premises (evidence), but a deductive argument cannot be. For example, this is a reasonably strong inductive argument: ...If the arguer believes that the truth of the premises definitely establishes the truth of the conclusion, then the argument is deductive."
Inductive Essay Examples
In inductive approach there is no theory at the beginning point of the research, and theories may evolve as a result of the research: It is noted that "inductive reasoning is often referred to as a "bottom-up" approach to knowing, in which the researcher uses observations to build an abstraction or to describe a picture of the ...
Deduction and Induction
Deductive and Inductive Arguments. Abstract: A deductive argument's premises provide conclusive evidence for the truth of its conclusion.An inductive argument's premises provide probable evidence for the truth of its conclusion. The difference between deductive and inductive arguments does not specifically depend on the specificity or generality of the composite statements.
Inductive Argument Examples
Example of Inductive Reasoning. Joe wore a blue shirt yesterday. Joe's shirt today is blue. Joe will wear a blue shirt tomorrow as well. Notice how the inductive argument begins with something specific that you have observed. It moves to a drawing a more general conclusion based on what you have observed in a specific instance (or in this case, on two specific days).
Inductive Argument, Essay Example
Analysis of inductive argument. This is a method of reasoning called induction. This method generally entails the use of inference method, collective principals, and law about a particular issue as well as argument. Moreover, in the case of opinion poll, it is the representation of a scientific research to rate the views of a given group.
"Inductive" vs. "Deductive"
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 ...

15 Inductive Reasoning Examples

Advantages and Disadvantages of Inductive Reasoning

Well-Formulated Inductive Reasoning Examples

2. Bonus Structure

3. Seasonal Trends

4. Archaeological Digs

5. Traffic Patterns

Poorly-Formulated Inductive Reasoning Examples

7. Job Salary and Occupation

8. Nationality

9. Left-Handedness

10. Rainy Weather

11. Buying Avocados

12. Food Poisoning

13. Buying A Mattress

14. Penguins

15. Rap Music

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Inductive Essays: Tips, Examples, and Topics

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2. What are the elements of an inductive essay?

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4. What is the difference between an inductive essay and a deductive essay?

5. How do I structure an inductive essay?

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1 Start with a Question or Guess

2 Establish Specific Premises

3 Make an Inductive Leap

4 Defend the Conclusion

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