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Inductive Reasoning | Types, Examples, Explanation

Published on January 12, 2022 by Pritha Bhandari . Revised on June 22, 2023.

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

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

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

Table of contents

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

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

Inductive Reasoning

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

Examples: Inductive reasoning
Stage Example 1 Example 2
Specific observation Nala is an orange cat and she purrs loudly. Baby Jack said his first word at the age of 12 months.
Pattern recognition Every orange cat I’ve met purrs loudly. All babies say their first word at the age of 12 months.
General conclusion All orange cats purr loudly. All babies say their first word at the age of 12 months.

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inductive reasoning problem solving

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

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

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

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

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

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

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.

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

Inductive generalizations are evaluated using several criteria:

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

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

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

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

Example: Statistical vs. non-statistical generalization
Specific observation 73% of students from a sample in a local university prefer hybrid learning environments. Most students from a sample in a local university prefer hybrid learning environments.
Inductive generalization 73% of all students in the university prefer hybrid learning environments. Most students in the university prefer hybrid learning environments.

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

A causal reasoning statement often follows a standard setup:

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

Good causal inferences meet a couple of criteria:

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

Sign reasoning involves making correlational connections between different things.

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

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

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

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

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

Analogical reasoning is also called comparison reasoning.

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

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

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

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

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

Inductive reasoning approach

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

Deductive reasoning approach

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

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.

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

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

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

Here are a few common types:

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

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving


Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

  • Concepts and inferences (7.1)
  • Procedural knowledge (7.1)
  • Metacognition (7.1)
  • Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
  • Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
  • Fixation:  functional fixedness, mental set  (7.3)
  • Algorithms, heuristics, and the role of confirmation bias (7.3)
  • Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

  • Identify which type of knowledge a piece of information is (7.1)
  • Recognize examples of deductive and inductive reasoning (7.2)
  • Recognize judgments that have probably been influenced by the availability heuristic (7.2)
  • Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

  • Use the principles of critical thinking to evaluate information (7.1)
  • Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
  • Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

  • Take a few minutes to write down everything that you know about dogs.
  • Do you believe that:
  • Psychic ability exists?
  • Hypnosis is an altered state of consciousness?
  • Magnet therapy is effective for relieving pain?
  • Aerobic exercise is an effective treatment for depression?
  • UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

  • Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
  • Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
  • Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

inductive reasoning problem solving

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

inductive reasoning problem solving

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

  • Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

  • What percentage of kidnappings are committed by strangers?
  • Which area of the house is riskiest: kitchen, bathroom, or stairs?
  • What is the most common cancer in the US?
  • What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

  • Statement #1: The forecaster on Channel 2 said it is going to rain today.
  • Statement #2: The forecaster on Channel 5 said it is going to rain today.
  • Statement #3: It is very cloudy and humid.
  • Statement #4: You just heard thunder.
  • Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

  • A particular class will be interesting/useful/difficult
  • You will be able to finish writing a paper by next week if you go out tonight
  • Your laptop’s battery will last through the next trip to the library
  • You will not miss anything important if you skip class tomorrow
  • Your instructor will not notice if you skip class tomorrow
  • You will be able to find a book that you will need for a paper
  • There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

  • What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

  • Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

  • Please take a few minutes to list a number of problems that you are facing right now.
  • Now write about a problem that you recently solved.
  • What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

  • Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
  • Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
  • Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
  • Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
  • Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
  • Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is Inductive Reasoning: The Key to Logical Thinking

Get a comprehensive understanding of ordinal data examples here. Learn about what ordinal data is, and how to use it in an effective way.

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Inductive reasoning is a fundamental cognitive process that plays a crucial role in our everyday lives and the scientific community. It draws general conclusions or makes predictions based on specific observations or evidence. Unlike deductive reasoning, which moves from general principles to specific instances, inductive reasoning moves in the opposite direction, from specific observations to broader generalizations.

This article provides a comprehensive understanding of inductive reasoning, its principles, and its applications across various domains.

What is Inductive Reasoning?

Inductive reasoning is a type of logical reasoning that forms general conclusions based on specific observations or evidence. It is a bottom-up approach where specific instances or examples are analyzed to derive broader generalizations or theories. In inductive reasoning, the conclusions are probabilistic rather than specific, as they are based on patterns and trends observed in the available evidence. 

The strength of the conclusions in inductive reasoning depends on the quality and quantity of the evidence, as well as the logical coherence of the reasoning process. Inductive reasoning is commonly used in scientific research and everyday life to make predictions, formulate hypotheses, and generate new knowledge or theories. It allows for the exploration and discovery of new ideas by building upon observed patterns and relationships in the data.

what is inductive reasoning

Types of Inductive Reasoning

The types of inductive reasoning provide valuable tools for making generalizations, predictions, and drawing conclusions based on observed evidence and patterns. Different types are commonly used to make inferences and predictions. Below are the main types:

Inductive Generalization

Inductive generalization refers to the process of inferring a general rule or principle based on specific instances or examples. It makes a generalized statement or conclusion about a whole population or category based on a limited sample or set of observations. Inductive generalization aims to extend the findings from specific cases to a broader context, providing a basis for making predictions or forming hypotheses.

Statistical Induction

Statistical induction, also known as statistical reasoning, is a method that draws conclusions about a population based on a statistical analysis of a sample. It has the principles of probability and statistical inference to make inferences and predictions about the larger population from which the sample was drawn. By analyzing the data collected from the sample, statistical induction allows researchers to estimate population parameters, test hypotheses, and make probabilistic statements about the likelihood of certain events or outcomes occurring.

Causal Reasoning

Causal reasoning seeks to understand the cause-and-effect relationships between variables or events. It identifies and analyzes the factors that contribute to a particular outcome or phenomenon. This type of reasoning establishes a cause-effect relationship by observing patterns, conducting experiments, or using statistical methods to determine the strength and direction of the relationship between variables. It helps researchers understand the underlying mechanisms behind an observed phenomenon and make predictions about how changes in one variable may affect another. 

Sign Reasoning

Sign reasoning, also known as semiotic reasoning, makes interpretation and analyzes signs, symbols, or indicators to draw conclusions or make predictions. It understands that certain signs or signals can signify or indicate the presence of a specific phenomenon or event. It observes and interprets patterns, relationships, or correlations between signs and the phenomena they represent. This allows researchers to uncover hidden meanings, infer intentions, and gain insights into human communication and expression. 

Analogical Reasoning

Analogical reasoning is a cognitive process that draws conclusions or makes inferences based on the similarities between different situations, objects, or concepts. It operates on the idea that if two or more things share similar attributes or relationships, they are likely to have similar properties or outcomes. Analogical reasoning allows individuals to transfer knowledge or understanding from a familiar or known domain to an unfamiliar or unknown domain. By recognizing similarities and making comparisons, analogical reasoning enables individuals to solve problems, make predictions, generate creative ideas, and gain insights. 

Examples of Inductive Reasoning

These examples illustrate how inductive reasoning can be applied in various contexts to draw conclusions, make predictions, and gain insights based on observed evidence and patterns:

If you observe that several cats you encounter are friendly and approachable, you may generalize that most cats are friendly. Another example is, if we observe that a few students in a class are diligent and hardworking, we may generalize that the entire class possesses these characteristics.

Based on survey data, if it is found that a majority of customers prefer a particular brand of smartphones, it can be statistically inferred that the brand is popular among the wider population. Or for instance, if a survey finds that a majority of respondents prefer a certain brand of coffee, we can statistically infer that the preference holds true for the broader population.

When studying the effects of exercise on weight loss, if you consistently find that participants who engage in regular exercise tend to lose more weight, you can infer that there is a causal relationship between exercise and weight loss. Another example, if studies consistently show a correlation between smoking and lung cancer, we can infer a causal link between the two.

If you notice dark clouds, strong winds, and distant thunder, you may infer that a storm is approaching. Or another example, doctors use various signs, such as fever, cough, and sore throat, to diagnose a common cold.

If you discover that a new medication is effective in treating a certain type of cancer, you may infer that a similar medication could be effective in treating a related type of cancer. 

Pros and Cons of Inductive Reasoning

What is inductive reasoning? Inductive reasoning refers to the cognitive process of drawing general conclusions based on specific observations or evidence. It is a valuable tool for making generalizations and predictions in various fields of study. But, like any reasoning method, inductive reasoning has its own set of pros and cons that are important to consider.

Exploring the advantages and limitations of inductive reasoning allows us to harness its strengths while being mindful of its potential shortcomings. Below are the pros and cons of Inductive Reasoning.

Pros of Inductive Reasoning

Flexibility: It allows for flexibility and adaptability in drawing conclusions based on observed patterns and evidence, making it suitable for exploring new or unfamiliar areas of knowledge.

Creative Problem Solving: It encourages creative thinking and the exploration of new possibilities by identifying patterns, connections, and relationships.

Hypothesis Generation: It can generate hypotheses or theories that can be further tested and refined through empirical research, leading to scientific advancements.

Real-World Application: It is often used in fields such as social sciences, market research, and data analysis, where generalizations and predictions based on observed patterns are valuable.

Cons of Inductive Reasoning

Potential for Error: It is susceptible to errors and biases, as conclusions are based on limited observations and may not account for all relevant factors or variables.

Lack of Certainty: It does not guarantee absolute certainty or proof. Conclusions drawn through induction are based on probabilities rather than definitive truths.

Sample Size and Representativeness: The reliability and generalizability of inductive reasoning depend on the sample size and representativeness of the observed data. A small or unrepresentative sample can lead to inaccurate conclusions.

Potential for Overgeneralization: Inductive reasoning can sometimes lead to overgeneralization, where conclusions are applied to a broader population without sufficient evidence, leading to inaccurate assumptions.

The Problem of Induction

The problem of induction is a philosophical challenge that questions the justification and reliability of inductive reasoning. It was famously addressed by the Scottish philosopher David Hume in the 18th century. The problem arises from the observation that inductive reasoning relies on making generalizations or predictions based on past observations or experiences. However, the problem of induction highlights that there is no logical or deductive guarantee that future events or observations will conform to past patterns.

This problem challenges the assumption that the future will resemble the past, which is a fundamental basis of inductive reasoning. But even if we observe a consistent pattern in the past, we cannot be certain that the same pattern will continue in the future. For example, if we observe the sun rising every day for thousands of years, it does not logically guarantee that it will rise tomorrow. The problem lies in the gap between observed instances and the generalization or prediction made based on those instances.

This philosophical challenge poses a significant hurdle for inductive reasoning because it undermines the logical foundation of drawing reliable conclusions based on past observations. It raises questions about the reliability, universality, and certainty of inductive reasoning. However, the problem of induction serves as a reminder to approach inductive reasoning with caution and to be aware of its limitations and potential biases. It highlights the need for critical thinking, rigorous testing, and the continual reassessment of conclusions to account for new evidence and observations.

Bayesian Inference

Bayesian inference is a statistical approach to reasoning and decision-making that updates beliefs or probabilities based on new evidence or data. It is named after Thomas Bayes, an 18th-century mathematician and theologian who developed the foundational principles of Bayesian inference.

At its core, Bayesian inference combines prior beliefs or prior probabilities with observed data to generate posterior beliefs or probabilities. The process begins with an initial belief or prior probability distribution, which represents our subjective knowledge or assumptions about the likelihood of different outcomes. As new evidence or data becomes available, Bayesian inference updates the prior distribution to yield a posterior distribution that incorporates both the prior beliefs and the observed data.

The theorem quantifies how the observed data supports or modifies our initial beliefs. By explicitly incorporating prior probabilities, it allows for a more nuanced and subjective approach to reasoning. It also facilitates the integration of new data as it becomes available, allowing for iterative updates and revisions of beliefs.

Inductive Inference

In inductive inference, we move from particular observations or examples to broader generalizations or hypotheses. Unlike deductive reasoning, which is based on logical deductions from premises to reach certain conclusions, inductive inference makes probabilistic judgments and draws probable conclusions based on the available evidence.

The process of inductive inference typically involves several steps. First, we observe or gather data from specific cases or instances. These observations could be qualitative or quantitative, and they provide the basis for generating hypotheses or generalizations. Next, we analyze the collected data, looking for patterns, trends, or regularities that emerge across the observations. These patterns serve as the basis for formulating generalized statements or hypotheses.

One common form of inductive inference is inductive generalization, where we generalize from specific instances to broader categories or populations. For example, if we observe that all the swans we have seen are white, we may generalize that all swans are white. However, it is important to note that inductive generalizations are not infallible and are subject to exceptions or counterexamples.

Another type of inductive inference is analogical reasoning, where we draw conclusions or make predictions based on similarities between different situations or domains. By identifying similarities between a known situation and a new situation, we can infer that what is true or applicable in the known situation is likely to be true or applicable in the new situation.

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Inductive Reasoning

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Inductive reasoning is a logical process in which multiple premises, all believed true or found true most of the time, are combined to obtain a specific conclusion or to supply evidence for the truth of a conclusion. Inductive reasoning is often used to generate predictions or to make forecasts. Inductive reasoning differs from deductive reasoning in that while the conclusion of a deductive inference is certain, the truth of the conclusion of an inductive inference is only probable, where the degree of certainty is based upon the strength (or consistency) of the evidence. In other words, the conclusion of an inductive inference is not a logical certainty (such as when a meteorologist predicts snow). Inductive reasoning also encompasses most cases of where a general principle is derived or where categories are formed based on specific observations (provided that they are probabilistic in nature).

In practice, inductive reasoning is the logical foundation of science, and all...

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Video transcript

Inductive Reasoning (Definition + Examples)

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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.

inductive reasoning

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.

survey says

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 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.

Related posts:

  • 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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Inductive Reasoning Tests: Example Questions & Tips ({YEAR})

What Are Inductive Reasoning Tests?

When are inductive reasoning tests used, what format do inductive reasoning tests take, how to prepare for an inductive reasoning test in 2024, frequently asked questions, inductive reasoning tests: example questions & tips (2024).

Updated May 15, 2024

Edward Mellett

Inductive reasoning tests are one of a range of psychometric tests often used during the recruitment process and are considered one of the more challenging aptitude tests.

Using inductive reasoning means using logical thinking to make a prediction or forecast behavior based on previous patterns.

When patterns are found, it can lead to a specific conclusion.

Typically, inductive reasoning tests are used to measure logic and problem-solving skills in job candidates.

Inductive reasoning tests are used early in the recruitment process in many different industries to narrow down applicants.

They are commonly used when recruiting for roles in engineering, IT and science.

The leading test providers for inductive reasoning tests are Kenexa and SHL.

Inductive reasoning tests assesses the candidate’s analytical and conceptual thinking skills and their ability to find solutions in a timed situation.

An inductive reasoning test is also known as a diagrammatic test or abstract reasoning test .

The test normally consists of a series of image-based patterns and will ask a candidate to identify the rule that links them.

Candidates who can capably problem solve, think logically and carry out tasks are an asset to an organization so these skills are highly valued by employers. Someone with strong inductive reasoning skills can:

  • Work efficiently and accurately – even under pressure
  • Spot inconsistencies, consistencies or rules in a series of objects or behavior patterns
  • Think logically and methodically in any situation

Prepare for Any Job Assessment Test with TestHQ

Some jobs that require inductive reasoning include law enforcement, corporate leaders, military, etc.

You would need to use your inductive reasoning skills to consider what the next step should be, find a solution or analyze a situation with limited information.

Inductive reasoning tests are used by employers to assess an employee as part of the recruitment process.

Usually, inductive reasoning tests are used in the recruitment process for technical roles or roles that require frequent problem-solving.

A psychometric test is standardized and provides measurable data.

They are useful when evaluating and understanding a candidate’s personality, thinking and analytical skills and providing an insight into their future performance in the job.

An inductive reasoning test is used to evaluate logical reasoning and problem-solving skills and a candidate’s ability to find solutions when under pressure.

An inductive reasoning test is formed of image or diagram-based questions.

The test taker needs to understand what is happening in these diagrams use this information to identify the next image in the sequence.

These questions are usually multiple-choice; you will usually be asked to pick from four to six very similar answer options.

The tests often have a strict time limit and may have a penalty for giving an incorrect answer.

The pressure of this situation also adds to the difficulties that candidates face in completing the test in the given time frame.

Inductive reasoning tests can vary based on the role and industry for which you are applying.

Most questions have some similarities, though, and will be aimed at testing job-related aspects.

Questions can relate to:

  • Reflections
  • Alternations
  • Translations
  • Replacements

Most will test your ability to infer a pattern based on previous information.

For example, you may be presented with five images featuring various shapes and will be asked to predict the next one.

The correct response requires you to understand the pattern by noting the similarities or differences or the relationship between the images.

You may also be asked to use similar techniques to identify what is missing from a sequence.

If you need to prepare for a number of different employment tests and want to outsmart the competition, choose a Premium Membership from TestHQ . You will get access to three PrepPacks of your choice, from a database that covers all the major test providers and employers and tailored profession packs.

Examples of Inductive Reasoning Tests (2024)

Example 1: patterns that move.

Inductive Reasoning Test

The image above presents five images of squares. Each square has a black dot within it that moves around the square. Choose which answer option (A, B, C, D or E) would appear next in the sequence.

Example 2: A Pattern Where the Relationship Rules Between Two Concepts Matter for the Solution

Inductive Reasoning Test

The image above presents shapes with black dots. Choose from A, B, C, D and E to determine which answer would come next in the sequence.

Example 3: Rotation

Inductive Reasoning Test

Choose the correct rotation in the sequence from A, B, C, D and E to determine the image that comes next.

To prepare for your inductive reasoning test, it is vital that you practice.

This will help you become familiar with the format and types of questions asked, increasing your confidence and helping you perform well.

You might even encounter some of the same patterns on test day.

As it is a timed test, practice completing the tests in real test conditions.

It is important that you get used to working quickly and accurately and feel some of that pressure in your practice.

On the day, bring a watch or stopwatch so you can manage your time and move on if needed.

Reflection and rotation are common questions in inductive reasoning tests.

Some people may find these difficult. One way to practice is to draw shapes and reflections and check your accuracy using a mirror.

What's an example of inductive reasoning?

Inductive reasoning is essentially making an informed or educated guess.

An example of inductive reasoning would be: Carly always leaves for work at 8:00 a.m. Carly is always on time. Carly assumes, then, that if she leaves at 8:00 a.m. for work today, she will be on time.

In contrast, deductive reasoning uses general ideas to form a specific conclusion: All interns arrive early. Carly is an intern. Therefore Carly should arrive early.

You might also have to apply inductive reasoning to images, shapes, and patterns. This will include:

  • Patterns that move
  • Patterns where the relationship rules between two concepts matter for the solution
  • Mirror images

What are types of inductive arguments?

The different types of inductive reasoning are:

  • Generalization – The people who get to work early have more productive days. Therefore those who turn up late are unproductive.
  • Statistical – 80% of the people I've met love drinking coffee. Therefore 80% of the global population like drinking coffee.
  • Casual – In the winter there is less footfall. Therefore winter will bring fewer customers.
  • Analogical – Sam and Mike are both left-handed and keep their computer mouse on the left of the keyboard. As Jill is also left-handed, she must keep her mouse on the left as well.
  • Predictive – We were very busy during the holiday season. So next holiday season, we will be just as busy.

How do I pass an inductive reasoning test?

To pass the inductive reasoning test , you should:

  • Take an educated guess if you do not know the answer by eliminating the definite incorrect options and choosing the most likely from the remaining options.
  • Read the instructions carefully
  • Don't take too long on a question
  • Pace yourself and keep a steady rhythm
  • Remain calm by using breathing techniques

How do I get prepared for the inductive reasoning test?

To prepare for the inductive reasoning test :

  • Familiarize yourself with the test procedure and question format
  • Complete practice tests online
  • Practice the process of elimination method
  • When completing practice questions, break each pattern down into smaller sections until you feel confident working with the whole image
  • Consider rotations, mirror images, and inconsistencies when trying to find the answer
  • Learn calming breathing techniques to help you in the test

Is the inductive reasoning test tough?

Yes, inductive reasoning tests can be tough. Solving the type of problems in these tests is not an everyday occurrence. The speed you have to answer also makes the test more challenging.

However, with the right amount of practice, you can pass an inductive reasoning test.

What are the 3 barriers to passing the inductive reasoning test?

The three barriers to passing the inductive reasoning test are:

  • Your natural ability
  • The amount of preparation you do before sitting the test
  • Your performance on the day – such as managing stress, applying the process of elimination, working through the questions at a steady pace.

Where can I get free inductive reasoning test training materials?

You can find free inductive reasoning test training materials at:

What are the top 3 expert tips to succeed in inductive reasoning tests?

The top three expert tips for inductive reasoning tests are:

  • Prepare as much as possible by completing practice tests under timed conditions
  • Understand and practice all the question formats
  • Manage your time and stress levels during the test

Why are inductive reasoning tests considered reliable?

Inductive reasoning tests are considered reliable because they measure a person's cognitive ability. As the answers are either right or wrong, there is no room for interpretation or bias.

How do I improve my inductive reasoning skills?

To improve your inductive reasoning skills , you can:

  • Start paying more attention to the smaller details of things, events and people around you. You cannot come to a conclusion without considering all the details.
  • Those with strong inductive reasoning skills can easily identify patterns. Make an effort to find patterns or behaviors and see if you can predict the outcome. Paying more attention will also allow you to quickly find patterns.
  • Draw your own conclusions and make predictions such as financial predictions or targets. Use the information you have around you and make informed choices. Once the outcome has happened, see how accurate you were, what you did right, and what you missed.
  • Improve your memory by taking more notes and increasing the number of details you can remember. Playing brain games and reading regularly will also help improve your memory.

One handy tip is that the employer/recruiter isn’t necessarily looking for test completion; instead, they are looking at the candidate’s ability to complete as much as they can accurately in a high-pressure situation.

Successful completion of an inductive reasoning test indicates that a job applicant can think analytically and work flexibly with new or unfamiliar information to find a solution.

Someone good at inductive reasoning can use observation to form a theory that can be used to make a future prediction or provide new solutions to an existing problem.

You might also be interested in these other Psychometric Success articles:

Logical Reasoning Tests: 2024 Guide for Logic Tests

Or explore the Aptitude Tests / Test Types sections.


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Guide To Inductive & Deductive Reasoning

Induction vs. Deduction

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

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

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

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

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

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Inductive Reasoning | Definition, Types, & Examples

Have you ever noticed how we generally conclude the world around us based on the things we observe? Maybe you’ve seen a red car speed by you every morning and concluded that all red cars are driven recklessly. Or perhaps you’ve tasted a delicious dish at a restaurant and assumed the rest of the menu must be amazing too. This way of thinking, where we move from specific observations to more general conclusions, is known as inductive reasoning which plays a crucial role in our daily lives and allows us to navigate uncertainty, make decisions, and learn from experience. But what exactly is this type of reasoning, and how does it work? Keep reading to know more.

Table of Content

What is Inductive Reasoning?

Types of inductive reasoning, how to improve your inductive reasoning, how to showcase your inductive reasoning skills.

Inductive reasoning is a way of thinking where you draw general conclusions from specific observations. It’s like climbing a ladder step by step, gathering evidence as you go, and then forming a broad picture based on what you’ve seen.

Here are some key points about inductive reasoning:

  • Starts with specific observations: Instead of relying on established rules or principles, you begin with individual pieces of information or data through experimentation, surveys, personal experiences, or simply observing the world around you. The more observations you have that support your pattern, the stronger your conclusion becomes.
  • Pattern identification: You analyze the collected data and look for patterns, repetitions, or similarities across the specific observations. This involves drawing connections and finding underlying trends.
  • Generalization: Based on the identified patterns, you form a general conclusion that applies to a broader category or situation. Your observations should be representative of the broader category you’re trying to generalize about. This is where you move from the “specific” to the “general.”
  • Conclusions are probable, not certain: Unlike deductive reasoning, where conclusions are guaranteed if the premises are true, inductive reasoning doesn’t offer absolute certainty. The more evidence you have, the stronger your conclusion, but there’s always a chance that you might encounter new information that contradicts it.

Here’s an example:

You observe that every swan you’ve ever seen is white. Based on this observation, you might inductively conclude that all swans are white. But, it’s important to remember that this conclusion is not guaranteed and you might encounter a black swan someday that challenges your assumption.

There are several different types of inductive reasoning, each with its own strengths and weaknesses. Here are some of the common types of Inductive Reasoning.

1. Inductive Generalization: This is the most basic type, where you observe specific instances and use them to draw a general conclusion about the entire population. But, it’s important to remember that just because something is true for some members of a group doesn’t mean it’s true for all.

Example: You see all the swans in your local pond are white, so you conclude that all swans are white.

2. Statistical Generalization: This type utilizes statistical data to generalize a population. It is more reliable than inductive generalization because it accounts for a larger sample size and considers the probability of error.

Example: A survey reveals that 80% of customers prefer brand A over brand B. This allows you to conclude that a majority of customers likely prefer brand A.

3. Causal Reasoning: This type involves identifying cause-and-effect relationships between events because it can help us understand the world around us, and it’s important to establish a strong correlation between the cause and effect before concluding.

Example: You notice your car engine overheats after driving with low oil. This leads you to believe that the low oil level caused the overheating.

4. Sign Reasoning: This type involves drawing conclusions based on signs or indicators that may not directly prove the conclusion, but they can suggest a link between two things.

Example: You see dark clouds in the sky, so you conclude that it might rain.

5. Analogical Reasoning: This type involves comparing two similar things and drawing a conclusion about one based on what is known about the other. While it can be useful for generating ideas and hypotheses, it’s important to remember that analogies are not perfect, and the differences between the two things might invalidate the conclusion.

Example: You observe that antibiotics are effective against bacterial infections, so you hypothesize that they might also be effective against viral infections.

Here are some ways you can improve your inductive reasoning skills:

  • Sharpen your observation skills: Pay close attention to your surroundings and actively seek out details by noticing patterns, trends, and anomalies. Ask yourself questions like “Why is this happening?” and “What could be causing this?”
  • Practice identifying patterns: Look for recurring elements, similarities, and connections between different things and try to identify the underlying rules or principles that govern these patterns.
  • Gather diverse data: Don’t rely on limited information but seek out different perspectives, viewpoints, and data sources to get a more complete picture.
  • Experiment and test your conclusions: Don’t just accept your initial inferences as true but try to test them out through experimentation, further observation, or research. Be open to revising your conclusions based on new evidence.
  • Engage in critical thinking: Don’t jump to conclusions but analyze all the available information, consider alternative explanations, and evaluate the strength of your evidence before drawing conclusions.
  • Practice with puzzles and games: Logical puzzles, riddles, and games like Sudoku can help you develop your pattern recognition and problem-solving skills, which are crucial for inductive reasoning.
  • Read and learn about different types of reasoning: Understanding different reasoning methods like deductive and inductive reasoning, as well as common fallacies, can help you identify and avoid biases in your thinking.
  • Discuss your reasoning with others: Share your observations and conclusions with others and listen to their perspectives. This can help you identify blind spots in your thinking and refine your conclusions.
  • Apply your skills in real-life situations: Actively use your inductive reasoning skills in everyday life when making decisions, solving problems, and understanding the world around you.

You can showcase your inductive reasoning skills by:

  • Use the STAR method: When answering interview questions about decision-making or problem-solving, use the STAR method (Situation, Task, Action, Result) to highlight a specific instance where you used inductive reasoning.
  • Quantify your results: If possible, quantify the positive results of your conclusions drawn through inductive reasoning to add concrete evidence to your claims and strengthen your case.
  • Tailor your examples: Choose examples relevant to the position you’re applying for. Demonstrating how your inductive reasoning skills benefited a similar role will resonate more with the interviewer.

Here are some examples of how you can showcase your inductive reasoning skills in technical interviews, tailored to different scenarios:

Scenario 1: Problem-solving based on data or logs

Situation: You are presented with a set of system logs from a recent crash. Task: Identify the root cause of the crash using inductive reasoning. Action: Analyse the logs for patterns, such as recurring error messages, specific timestamps, or correlations between events. Draw inferences based on these patterns (e.g., “This error message typically precedes the crash, suggesting a potential culprit”). Result: You pinpoint the root cause of the crash, saving time and resources for debugging.

Scenario 2: Design decisions based on user behavior

Situation: You are given anonymized user behaviour data from a new software feature. Task: Recommend design improvements based on user interactions. Action: Identify patterns in user actions, such as frequently used functionalities, areas of confusion, or abandoned workflows. Draw conclusions about user needs and preferences (e.g., “Users seem to struggle with this specific menu, suggesting a redesign”). Result: You suggest targeted design changes that improve user experience and engagement.

Scenario 3: Code optimization based on performance analysis

Situation: You are presented with performance profiling data from an application. Task: Identify bottlenecks and suggest optimization strategies. Action: Analyse the data for patterns like resource-intensive functions, slow database queries, or inefficient algorithms. Draw conclusions about performance-impacting areas (e.g., “This function seems to be called excessively, potentially causing slowdowns”). Result: You propose optimization strategies that improve application performance and efficiency.

In a nutshell, Inductive reasoning isn’t a static process but it’s a journey of exploration and continuous learning. By actively seeking diverse data, and constantly refining your conclusions, you can unlock the power of inductive reasoning to gain deeper insights and make informed decisions.

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Information & Contributors

Bibliometrics & citations, view options, recommendations, temporal knowledge graphs reasoning with iterative guidance by temporal logical rules.

Reasoning is essential for the development of large temporal knowledge graphs, which aim to infer new facts based on existing ones. Recent temporal knowledge graph reasoning methods mainly embed timestamps into low-dimensional spaces. ...

Multi-hop temporal knowledge graph reasoning with temporal path rules guidance

Temporal Knowledge graph (TKG) is a key component of artificial intelligence. In recent years, many large-scale temporal knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods ...

Reasoning by coordination: an integration of case-based and rule-based reasoning systems

A case-based reasoning system (CBRS) is appropriate for an experience-rich domain, while a rule-based system performs reasonably well in a knowledge-rich application environment. A CBRS mainly uses past experiences as problem-solving tools, and ...


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