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• What Is Deductive Reasoning? | Explanation & Examples

What Is Deductive Reasoning? | Explanation & Examples

Published on January 20, 2022 by Pritha Bhandari . Revised on June 22, 2023.

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

Deductive reasoning is also called deductive logic or top-down reasoning.

Table of contents

What is deductive reasoning, validity and soundness, deductive reasoning in research, deductive vs. inductive reasoning, other interesting articles, frequently asked questions about deductive reasoning.

In deductive reasoning, you’ll often make an argument for a certain idea. You make an inference, or come to a conclusion, by applying different premises.

A premise is a generally accepted idea, fact, or rule, and it’s a statement that lays the groundwork for a theory or general idea. Conclusions are statements supported by premises.

Deductive logic arguments

In a simple deductive logic argument, you’ll often begin with a premise, and add another premise. Then, you form a conclusion based on these two premises. This format is called “premise-premise-conclusion.”

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Validity and soundness are two criteria for assessing deductive reasoning arguments.

In this context, validity is about the way the premises relate to each other and the conclusion. This is a different concept from research validity .

An argument is valid if the premises logically support and relate to the conclusion. But the premises don’t need to be true for an argument to be valid.

• If there’s a rainbow, flights get canceled.
• There is a rainbow now.
• Therefore, flights are canceled.
• All chili peppers are spicy.
• Tomatoes are a chili pepper.
• Therefore, tomatoes are spicy.

In an invalid argument, your premises can be true but that doesn’t guarantee a true conclusion. Your conclusion may inadvertently be true, but your argument can still be invalid because your conclusion doesn’t logically follow from the relationship between the statements.

• All leopards have spots.
• My pet gecko has spots.
• Therefore, my pet gecko is a leopard.
• All US presidents live in the White House.
• Barack Obama lived in the White House.
• Therefore, Barack Obama was a US president.

An argument is sound only if it’s valid and the premises are true. All invalid arguments are unsound.

If you begin with true premises and a valid argument, you’re bound to come to a true conclusion.

• Flights get canceled when there are extreme weather conditions.
• There are extreme weather conditions right now.
• All fruits are grown from flowers and contain seeds.
• Tomatoes are grown from flowers and contain seeds.
• Therefore, tomatoes are fruits.

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

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

This method is used for academic as well as non-academic research.

Here are the general steps for deductive research:

• Select a research problem and create a problem statement.
• Develop falsifiable hypotheses .
• Collect your data with appropriate measures.
• Analyze and test your data.
• Decide whether to reject your null hypothesis .

Importantly, your hypotheses should be falsifiable. If they aren’t, you won’t be able to determine whether your results support them or not.

You formulate your main hypothesis : Switching to a four-day work week will improve employee well-being. Your null hypothesis states that there’ll be no difference in employee well-being before and after the change.

You collect data on employee well-being through quantitative surveys on a monthly basis before and after the change. When analyzing the data, you note a 25% increase in employee well-being after the change in work week.

Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach.

In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using inferential statistics and form a conclusion.

Inductive reasoning is also called a hypothesis-generating approach, because you start with specific observations and build toward a theory. It’s an exploratory method that’s often applied before deductive research.

In practice, most research projects involve both inductive and deductive methods.

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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

Deductive reasoning is also called deductive logic.

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

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

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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.

What is Deductive Reasoning?

Have you ever heard of something called deductive reasoning?

Even if you haven't heard of it, there’s a good chance you do it every single day .

Deductive reasoning is a logical type of deductive inference—or a way of thinking that helps you figure out things in a logical and smart way. In simpler words, deductive reasoning helps you make smart guesses based on what you already know is true. 

Want to discover more about it and how can you improve it? Keep reading to learn more. 

Imagine you have a bunch of puzzle pieces (these are facts or information). You also have a picture on the puzzle box (this is a general rule). Then you use these puzzle pieces to create the picture just like on the box. 

Deductive reasoning starts with these general rules or facts that we believe are true. Then, we use these rules to come up with inferences based on new ideas or conclusions. If the rules are true, then the conclusion based on what you come up with must also be true. 

Here’s an example: Let’s say you know that “all cats have tails” and you also know that “Fluffy is a cat.” Using deductive reasoning, you can then conclude that “Fluffy has a tail.” This is because the rule says all cats have tails, and since Fluffy is a cat, she must have a tail. 

Deductive reasoning has three steps: 

Step 1: Starting with what you know: You begin with some things you believe are true. These things can be stuff you’ve seen, things you’ve learned before, or general conclusions. 

Step 2: Figuring something out: You can use these true things to figure out something new. This new thing should also be true if the first things are true. 

Step 3: Checking if it makes sense: Finally, you make sure that the new thing you figured out actually makes sense based on the true things you started with. 

Deductive vs Inductive Reasoning: What's the Difference?

There’s a chance you might have heard of inductive reasoning—but it’s not the same as deductive reasoning. Inductive and deductive reasoning are two distinct approaches to logical conclusion-making. 

Here are some key differences: 

Deductive reasoning is like starting with a big idea and using it to figure out smaller things, or having a rule and using it to solve problems. For example, if you know that “all fish live in water” and you also know “Nemo is a fish,” you can use deductive reasoning to figure out that “Nemo lives in water.” You’re using the general rule to find a specific answer. 

Inductive reasoning , on the other hand, is the opposite. You start with lots of specific things, like observations, and then you come up with a general idea based on those specifics. It’s like noticing a bunch of fish in water and then thinking, “Hey, maybe all fish live in water.” So, you’re using many examples to make a big idea. 

The main difference between deductive reasoning and inductive reasoning is that in deductive reasoning, you use a rule to solve puzzles or find specific answers, while with inductive reasoning, you spot patterns to come up with new ideas. Both are used to understand things better. 

Scientists often use both of these ways to learn about the world and make smart conclusions, but even if you’re not a scientist, you can too. 

The Significance of Deductive Reasoning in Daily Life

Deductive reasoning is a useful tool you can use in everyday life to make choices and solve problems. And whether you realize it or not, you use deductive reasoning already when you think about things based on facts you know. 

For example, if it’s cloudy and you’re wondering whether to take an umbrella, you might remember that it’s rained a lot lately and think, “Maybe I should take the umbrella today.” You’re taking what you know and using it to make a decision. 

When things get tricky, like solving a puzzle or a tough question, deductive reasoning can help, too. You can break down the big problem into smaller parts and figure them out one by one. It’s like solving a big puzzle by focusing on each piece one at a time. 

Deductive reasoning is particularly important in professions such as law, where lawyers use it to draw conclusions from evidence presented in court. By applying deductive reasoning to the facts of a case, lawyers can build a logical argument that supports their client's position.

In science, deductive reasoning is used to draw conclusions from experiments and observations. Scientists use deductive reasoning to develop hypotheses and theories that can be tested through experimentation. By starting with a general set of premises, or principles, and using deductive reasoning to draw specific conclusions, scientists can develop a deeper understanding of the natural world.

The 3 Types of Deductive Reasoning

There are three types of deductive reasoning: categorical deduction, propositional deduction, and predicative deduction. Let’s break them down: 

1. Categorical Deduction: Reasoning within Categories

Categorical deduction involves putting things into groups and making smart decisions based on those groups. Imagine you have a rule like “all dogs bark,” and you know your pet is a dog. You can use categorical deduction to decide that your pet barks. This helps us in many jobs like law and science, where you need to make solid, evidence-based arguments and choices.

2. Propositional Deduction: Exploring If-Then Scenarios

Propositional deduction involves exploring if-then scenarios and drawing conclusions based on them. It’s like saying, “If I do my homework, then I can play games.” If you know that “if it’s sunny, the ground dries up,” and you see that the ground is dry, you can figure out that it must have been sunny. People use this technique in debates and discussions to make strong, persuasive points. 

3. Predicative Deduction: Drawing Conclusions from Premises

Predicative deduction involves looking at premises, or examples, to understand things better. So, if you know the premise that “all birds have feathers,” and you know Tweety is a bird, you can conclude that Tweety must have feathers. 

All of these types of deductive reasoning are really useful for making good decisions, solving problems, and learning more about the world.

Logically Sound Deductive Reasoning: Examples and Analysis

So, let’s say you’re solving a puzzle. You have some facts handy, and you want to figure out something new. Logically sound deductive reasoning, or making conclusions based on true facts, helps you do that in a smart way, because it allows you to draw accurate conclusions based on factual information. 

But to understand deductive reasoning better, you need to look at how good these facts are—and this is done by analyzing the validity and soundness of deductive arguments.

Analyzing the Validity and Soundness of Deductive Arguments

The validity and soundness of a deductive argument are two important criteria for evaluating its quality. 

• Validity: An argument is valid when the conclusion must be true based on the facts you have. It’s like putting puzzle pieces together in a way that fits perfectly. For instance, if you know “all roses have thorns” and “the flower you're holding is a rose,” it’s valid to say “the flower you're holding has thorns.”
• Soundness: An argument is sound when it’s not only valid but is also based on true facts. So, if you can prove that “all roses have thorns” and that “the flower you're holding is a rose,” then this argument is both valid and sound because the facts are true. 

However, an argument can be valid but not sound if the facts are wrong. For example, if you think “all bears can fly” and “Teddy is a bear,” it’s I to say “Teddy can fly,” but it’s not sound because bears can’t actually fly. 

Logically Unsound Deductive Reasoning: Examples and Explanation

Now, let’s say you’re imagining the same puzzle as before, but someone gives you the wrong pieces. Logically unsound deductive reasoning is like trying to make sense of things using those wrong puzzle pieces. It can lead you to wrong conclusions and isn’t a good way to think. 

Here’s an example: Let’s say someone tells you “all dogs have superpowers” and then says “Rover is a dog, so Rover has superpowers.” This is not a good way to think because, as much as we would love to see this in real life, the whole first part of the statement is wrong. 

Another example is when someone says “everyone who wears glasses is a genius” and then says “Emma wears glasses, so Emma is a genius.” This isn’t a good way to think either because not all people who wear glasses are geniuses. 

When you use false facts or make mistakes in your thinking, you can end up with wrong ideas, which may ultimately lead to confusion and bad decisions. To think clearly and make good choices, it’s important to do your homework and use true facts that fit together properly.  

Common Mistakes in Deductive Reasoning and How to Avoid Them

Deductive reasoning is a great tool for critical thinking, but it’s also prone to several common mistakes. Here are a few and how to avoid them: 

• Assuming that the premises are true without real evidence: To avoid this, always check if the facts are true before making a decision. 
• Using invalid, or wrong arguments that don’t logically make sense: To fix this, use an intentional way of thinking that really fits the situation. 
• Ignoring facts that don’t agree with what you think: To fix this, consider all sides of the story before making up your mind.
• Using tricky ways of reasoning, such as circular reasoning or ad hominem attacks: To avoid this, always make sure your line of thinking makes sense. 

These mistakes can have a significant impact on the accuracy and reliability of deductive reasoning. 

Want to avoid these mistakes in the future? Here are our tips:

• Check if the facts are true before believing them.
• Use a way of thinking that fits the situation. 
• Think about all the different ideas and facts. 
• Use smart and clear ways of thinking, not tricky ones. 

Improving Your Deductive Reasoning Skills: Practical Tips

There are several practical tips for improving your deductive reasoning skills, including practicing regularly, being mentally flexible, and using games and activities that boost deductive reasoning. By honing your deductive reasoning skills, you can become a more effective problem solver and critical thinker.

5 Useful Strategies to Enhance Your Deductive Reasoning

Some useful strategies to enhance your deductive reasoning include:

• Breaking down complex problems into smaller parts to better understand the facts
• Considering all possible scenarios and counterexamples to ensure that the conclusion is accurate and reliable
• Using diagrams, charts, or other visual aids to help organize information and identify patterns
• Practicing regularly and consistently to build your deductive reasoning skills over time
• Being mentally flexible and open-minded to new ideas and perspectives

Engaging with Games and Activities that Boost Deductive Reasoning

Another strategy? Games. There are several games and activities that can help boost deductive reasoning, including the following:

• Sudoku: Sudokus are number-based puzzle games that challenge your mind to think logically and systematically.
• Crossword puzzles: Crossword puzzles are word-based puzzle games that require deduction and problem-solving skills.
• Logic puzzles: Logic puzzles require you to use deductive reasoning to determine the correct sequence of events or relationships between objects.

All these games help improve deductive reasoning by challenging the mind to think logically and draw accurate conclusions from premises. Regular practice with these activities can help you build your deductive reasoning skills and become a more effective problem-solver and critical thinker.

Final Thoughts on Deductive Reasoning: A Tool for Critical Thinking

As you can see, deductive reasoning is a valuable tool for critical thinking. It helps you make smart decisions based on true facts. By learning about different types of deductive reasoning and avoiding common mistakes, you can get even better at solving problems and thinking really well.

Just like practicing a sport or playing an instrument, practicing deductive reasoning exercises help you get better at it. The more you practice, the better you become at using deductive reasoning effectively. And if you’re looking for an easy and fun way to do this, check out the Elevate app. 

With the Elevate app, you can make exercising your deductive reasoning skills a habit through personalized brain training workouts, plus 40+ games backed by science and designed to improve your vocabulary , mental math, memory skills , and more. 

Download Elevate on iOS or Android now, and start flexing your critical thinking muscles today!

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Deductive Reasoning (Definition + Examples)

At the age of 11 or 12, children enter what famed psychologist Jean Piaget identified as the formal operational stage. While this represents the last of Piaget's stages of cognitive development, it's important to note that development continues in various aspects throughout life, including emotional, moral, and social dimensions. In the formal operational stage, children begin to think abstractly and can apply these abstract thoughts to problem-solving. It's during this stage that they also become acquainted with a process known as deductive reasoning.

Deductive reasoning is a process in which we conclude the world around us. It’s also one of the basic ideas introduced to students learning about logic and how to form an argument. Deductive reasoning can help us discover the truth, but as you’ll see in the video, sometimes this process is done so quickly because it’s obvious.

On this page, I will discuss deductive reasoning, how we use it in everyday life, and how it differs from inductive reasoning. Understanding deductive and inductive reasoning are essential building blocks for understanding how we make sense of the world and how we make decisions.

Top-Down vs. Bottom-Up Logic

When discussing reasoning and logic, two commonly used terms are "top-down" and "bottom-up." These terms refer to the direction or flow of information or reasoning.

• Top-Down Logic (Deductive Reasoning) : This method begins with a general statement or hypothesis and examines the possibilities to reach a specific, logical conclusion. It's like starting from a broad perspective and narrowing it down. In essence, if the broader generalization is true, then the specific conclusion must also be proper.
• Bottom-Up Logic (Inductive Reasoning) : This is the opposite of the top-down approach. It starts with specific observations and measures, begins to detect patterns and regularities, formulates some tentative hypotheses that we can explore, and finally develops some general conclusions or theories. Instead of starting with a broad generalization, we collect bits of data and form a general conclusion based on those observations.

Understanding Correlation in Reasoning

Another important concept to grasp when discussing reasoning is the idea of correlation. Correlation refers to a relationship or association between two or more variables. When two variables tend to change together consistently, they are said to be correlated.

• Correlation and Inductive Reasoning : Often, inductive reasoning involves observing correlations in the real world. For instance, we might observe that when one event happens, another event tends to follow. However, it's crucial to understand that correlation does not imply causation. Just because two variables change together doesn't mean one causes the other. Distinguishing between mere correlation and actual causation is vital for forming accurate conclusions based on observations.

For example, there might be a correlation between ice cream sales and the number of drowning incidents in a given area. While these two variables are correlated (both increase during the summertime), one does not cause the other. Instead, an external factor, like hotter weather, affects both variables.

What is Deductive Reasoning?

Deductive reasoning, or deduction, is the process of using a group of true premises to draw a conclusion that is also true. This is also known as “top-down logic” because it takes broad statements and uses them to create more narrow statements.

Here’s an example of deductive reasoning.

Premise A says that all dogs are good boys.

Premise B says that Kevin is a dog.

The conclusion that we draw from deductive reasoning says that Kevin is a good boy.

Of course, that example is silly, but it shows how we can use two ideas and deductive reasoning to form an argument or a statement. Other examples of premises like this include “all dogs are mammals” or “every human embryo is made from sperm and an egg.”

Premise A is typically a very broad and general statement. Premise B is a more narrow statement that relates to Premise A. The conclusion states a narrow truth relating to Premise A and Premise B.

Characteristics of deductive reasoning

To start the deductive reasoning process, you must use a statement that we all know to be true. If the statement is not true, or true sometimes , you may still be able to form a conclusion through induction. But to use deductive reasoning, that truth must be as solid as concrete.

It will also have to funnel down to make a more narrow conclusion through entailment. Premises A and B must be related so that Premise C can exist. Let’s go back to our example.

In both Premise A and Premise B, dogs are mentioned. Premise C grabs a conclusion from both premises in a logical, relevant way. When any of these parts of the deduction don’t follow the rules, problems may ensue.

The rules of deductive reasoning are airtight. If you’re not following them, you’re not using deductive reasoning. This may not change the validity of the premises or the conclusions you draw from your premises, but it does change whether or not it falls under the category of deductive reasoning.

If any of the following exist, you might end up coming to a false conclusion:

• False premises
• Lack of entailment
• A narrow truth

False Premises

Let’s go back to the idea that all dogs are good boys. In this case, one can unfortunately argue that not all dogs are good boys. This would automatically make the conclusion untrue. A conclusion is only considered the truth when the premises that precede it are true.

Notice here that we said that the conclusion is untrue. You may argue that Kevin is a good boy, even though not all dogs are. That means that the conclusion is valid. In philosophy, validity and truth are not the same thing.

So while some dogs are good boys, Kevin is a dog, and Kevin is a good boy, this is not a conclusion you can draw through deductive reasoning as ancient philosophers laid it out.

Lack of Entailment

Here’s another problem with deductive reasoning that we run into a lot. For a conclusion to be true, the premises that precede it directly support and lead to the conclusion.

Here’s an example of how failing to use this rule can create a weak conclusion. (Let’s go back to pretending that “all dogs are good boys” is a known fact.)

The conclusion drawn from this is that Kevin has blue eyes.

Kevin could very well have blue eyes, but just because the conclusion is valid doesn’t mean it is true because we have nothing to support the idea that Kevin’s eyes are blue.

Remember, you have to reach this conclusion through entailment. No premise has anything to do with the color of Kevin’s or any dog’s eyes. So we can’t come to that conclusion based on the premises given to us.

Narrow Truth

Think of all of the things that you know as true. Surprisingly, these broad and general facts are not easy to come by. And when they do, they seem too obvious to use in an example.

So deductive reasoning also seems very obvious, and outside of being the basis of forming an argument, it’s not useful in everyday life.

Let’s use another example of deductive reasoning, shall we?

Premise A says that all humans live on land.

Premise B says that Megan is a human.

The conclusion that you would get from deductive reasoning says that Megan lives on land.

Well, yeah. Duh. She’s a human, after all.

Deductive reasoning comes naturally to us. We do it without thinking. To figure out that a human lives on land or that a dog is a mammal is a quick process when you already know that all dogs are mammals and that all humans live on land.

However, due to the nature of deductive reasoning, you need those broad truths to conclude from. A more narrow truth won’t give you much to work with.

Example 1: All humans are mortal. Susan is a human. Susan is mortal.

This is a classic example of deductive reasoning. It starts with an entirely true statement - you can’t poke holes in it or argue against it. (Maybe in a few decades, you can, but not today!) The next statement is also true and ties into the first statement. The conclusion brings both statements together to create a statement that we have now proven is true.

Example 2: Marketing

In everyday life, we don’t always use deductive reasoning using the strict rules of traditional logic. Marketers, for example, may use deductive reasoning to make decisions about how they want to advertise their products to certain groups of customers.

They may use information from focus groups or surveys to create a profile of their products. Let’s say a company that makes cleaning products wants to target single women in their late 20s who are upper-middle-class. They collect information about the demographic and learn that upper-middle-class single women in their late 20s find more valuable products with natural ingredients and are “green.”

Premise 1 is that upper-middle-class women in their 20s find more value in products that have natural ingredients and are “green.”

Premise 2 is that the company’s target audience is upper-middle-class women in their 20s.

The marketers conclude that if they brand their products as “green” and highlight their natural ingredients, their target audience will find more value in their products.

Again, this doesn’t exactly fit the rules of “top-down logic.” Not every upper-middle-class woman particularly cares what is in their cleaning products. And not every upper-middle-class woman is in the company’s target audience. But this is often how we use deductive reasoning to conclude. These conclusions can still be very helpful, even if the conclusions aren’t 100% true.

Example 3: Deductive Reasoning in Math

Deductive reasoning is introduced in math classes to help students understand equations and create proofs. When math teachers discuss deductive reasoning, they usually talk about syllogisms. Syllogisms are a form of deductive reasoning that helps people discover the truth.

Here’s an example.

The sum of any triangle’s three angles is 180 degrees.

You are given a triangle to work with.

You can conclude that the sum of the triangle’s three angles is 180 degrees.

This conclusion will help you move forward when working with the triangle and discovering the length of each side or the measurement of each angle.

Example 4: Deductive Reasoning in Science

Deductions and induction are used to prove hypotheses and support the scientific method. Deduction requires us to examine how closely the premises and the conclusion are related. If the premises are backed by evidence and experiment, the conclusion will likely be true.

In the scientific method, scientists form a hypothesis. They then conduct experiments to see whether that hypothesis is true. With each experiment, they prove the strength of the premises and support their conclusion about whether or not their hypothesis is correct.

Without deductive reasoning, scientists may come to untrue conclusions or accept things that are likely as true things.

Deductive vs inductive reasoning

At the beginning of this video, I mentioned that child psychologist Jean Piaget theorized that children develop the skills of deductive reasoning around 11 or 12 years old. From then on, it’s not exactly something that we think about.

So we’re more likely to conclude things in the opposite direction. We use inductive reasoning to make sense of the world around us. We take a single experience or a few experiences from the past to conclude what might happen in the immediate future or indefinitely.

Inductive reasoning is more prevalent in our everyday lives because it requires a personal experience or a handful of facts. Getting down to the “truth,” especially if you are a philosopher or someone who is especially skilled in logic, is not always an easy thing to do. Plus, deductive reasoning doesn’t usually give us any incentive or confidence to take action. It just helps us build the world.

But I’ll talk more about inductive reasoning in my next video. I’ll break down what inductive reasoning is, the different types of inductive reasoning we use in everyday life, and the problems that come with inductive reasoning.

Have you been listening? Let’s test your knowledge with a quick, three-question quiz on deductive reasoning.

First question:

Is deductive reasoning considered “top-down” or “bottom-up” logic?

“Top-down logic.” It starts with broad truths and goes down to a more narrow conclusion. “Bottom-up logic” is called induction. 

Second question:

What can interfere with deduction?

A: False premises

B: Lack of entailment

C: Narrow truth

D: All of the above

All of the above! To arrive at the truth, you must provide true premises that logically lead to the conclusion. This means starting with a very broad truth and making your way down.

Last question: does this “count” as deductive reasoning?

Premise 1: All pigeons are birds.

Premise 2: John is a pigeon.

Conclusion: John is a bird.

Yes, it counts! All of the premises are true and contribute to the conclusion, which is also true.

Related posts:

• Inductive Reasoning (Definition + Examples)
• Circular Reasoning (29 Examples + How to Avoid)
• The Psychology of Long Distance Relationships
• Operant Conditioning (Examples + Research)
• Beck’s Depression Inventory (BDI Test)

Reference this article:

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What Is Deductive Reasoning?

Definition & Examples of Deductive Reasoning

How Deductive Reasoning Works

Deductive reasoning vs. inductive reasoning, examples of deductive reasoning, benefits of deductive reasoning.

Deductive reasoning is a type of logical thinking that starts with a general idea and reaches a specific conclusion. It's sometimes is referred to as top-down thinking or moving from the general to the specific.

Learn more about deductive reasoning and its value in the workplace.

Deductive reasoning is a form of logical thinking that's widely applied in many different industries and valued by employers. It relies on a general statement or hypothesis—sometimes called a premise—believed to be true. The premise is used to reach a specific, logical conclusion.

A common example is the if/then statement. If A = B and B = C, then deductive reasoning tells us that A = C.

• Alternate name : Deduction

With deductive reasoning, premises are used to reach a conclusion. For example, a marketing manager might realize that their department is going over budget on advertising. After reviewing the numbers, they observe that while the company's Facebook advertisements get a lot of clicks, they have a higher number of contacts through their email list.

The manager decides to reduce Facebook advertising to stay under budget and focus on getting consumers to sign up for their email list. Over the next quarter, the department stays under budget and sales are steady.

The manager followed the deductive reasoning process. Here's how deductive reasoning in the workplace typically works:

• Clarify the issue, making sure to understand what's at stake.
• Look at data relating to the issue, asking questions.
• Formulate a hypothesis, which is a possible reason for the issue.
• Test the hypothesis by implementing a solution that resolves the reason for the issue.
• Evaluate your results, repeating the steps until the desired results are achieved.

Deductive reasoning differs from inductive reasoning , sometimes known as bottom-up thinking. Inductive thinking starts with specific observations which are used to reach a broad conclusion. Deductive reasoning starts with broad observations which are used to reach specific conclusions.

Deductive reasoning is an important skill in many different jobs and industries. For example, it's particularly useful for people in management positions who have to make critical business decisions every day.

If you're looking for a new position, highlighting your deductive reasoning can show employers you know how to use logic to benefit the organization.

To prepare, think of ways you've used deductive reasoning in the workplace. Consider these examples:

• Based on market research, a marketing team believes that professional women are overloaded with family and work responsibilities and strapped for time. They decide to advertise that their hair coloring product can be applied in less time than their competition's hair coloring product. They see a modest increase in sales.
• A human resources department has identified public speaking skills as an important qualifier for a particular position. They decide to require candidates to make an oral presentation on a predetermined topic as a part of their second interview. The candidate they decide to hire proves successful in this aspect of their work.
• After reviewing their numbers, development executives at a college believe that professionals working in the financial sector are the best donors. They direct their two most effective staff members to target alumni working in finance when it comes time to plan their next fundraising strategy. 
• A liquor store owner identifies a trend that customers are buying more bourbon than other types of alcohol. The store owner then allocates prime ad space to bourbon and offers related discounts.
• Detectives believe that robberies at banks are usually inside jobs planned by experienced thieves. To narrow down their suspects after a bank robbery, they decide to do criminal background checks on employees with access to cash reserves. 

You can develop your deductive reasoning skills by developing your knowledge base through reading and research and by doing puzzles that challenge you to see new patterns.  

Deductive reasoning allows you to use logic to justify work-related decisions. Even when the decision doesn't work out, you can explain why you decided to do what you did. Being able to use deductive reasoning is valuable to employers. Employers value decisive, proactive employees.

When applying for jobs, it's a good idea to highlight your deductive reasoning skills. This is particularly important if you're applying for a managerial position in which you will have to make important decisions that will affect the organization.

You don’t need to include the phrase “deductive reasoning” on your job materials unless it's a specific requirement of the job. Instead, you might mention in your cover letter or resume an example of when you used deductive reasoning to benefit your organization. Specific examples will clearly show employers how you use your logic to bring value to the company you work for.

Key Takeaways

• Deductive reasoning starts with a general idea and reaches a specific conclusion. 
• It's a form of logical thinking that's valued by employers. 
• You may use deductive reasoning without realizing it to make decisions about your work. 
• It's an important skill to highlight by providing examples in your cover letter, resume, or during your interview. 

Motlow State. " Deductive and Inductive Reasoning ." Accessed June 28, 2020.

Cleverism. " Learn Why Employers Value Deductive Reasoning, and How You Can Show It ." Accessed June 28, 2020.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

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

READING WITH PURPOSE

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.

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:

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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Deductive, Inductive and Abductive Reasoning

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TIP Sheet DEDUCTIVE, INDUCTIVE, AND ABDUCTIVE REASONING

Reasoning is the process of using existing knowledge to draw conclusions, make predictions, or construct explanations. Three methods of reasoning are the deductive, inductive, and abductive approaches.

Deductive reasoning: conclusion guaranteed Deductive reasoning starts with the assertion of a general rule and proceeds from there to a guaranteed specific conclusion. Deductive reasoning moves from the general rule to the specific application: In deductive reasoning, if the original assertions are true, then the conclusion must also be true. For example, math is deductive:

If x = 4 And if y = 1 Then 2x + y = 9

In this example, it is a logical necessity that 2x + y equals 9; 2x + y must equal 9. As a matter of fact, formal, symbolic logic uses a language that looks rather like the math equality above, complete with its own operators and syntax. But a deductive syllogism (think of it as a plain-English version of a math equality) can be expressed in ordinary language:

If entropy (disorder) in a system will increase unless energy is expended, And if my living room is a system, Then disorder will increase in my living room unless I clean it.

In the syllogism above, the first two statements, the propositions or premises , lead logically to the third statement, the conclusion . Here is another example:

A medical technology ought to be funded if it has been used successfully to treat patients. Adult stem cells are being used to treat patients successfully in more than sixty-five new therapies. Adult stem cell research and technology should be funded.

A conclusion is sound (true) or unsound (false), depending on the truth of the original premises (for any premise may be true or false). At the same time, independent of the truth or falsity of the premises, the deductive inference itself (the process of "connecting the dots" from premise to conclusion) is either valid or invalid . The inferential process can be valid even if the premise is false:

There is no such thing as drought in the West. California is in the West. California need never make plans to deal with a drought.

In the example above, though the inferential process itself is valid, the conclusion is false because the premise, There is no such thing as drought in the West , is false. A syllogism yields a false conclusion if either of its propositions is false. A syllogism like this is particularly insidious because it looks so very logical–it is, in fact, logical. But whether in error or malice, if either of the propositions above is wrong, then a policy decision based upon it ( California need never make plans to deal with a drought ) probably would fail to serve the public interest.

Assuming the propositions are sound, the rather stern logic of deductive reasoning can give you absolutely certain conclusions. However, deductive reasoning cannot really increase human knowledge (it is nonampliative ) because the conclusions yielded by deductive reasoning are tautologies -statements that are contained within the premises and virtually self-evident. Therefore, while with deductive reasoning we can make observations and expand implications, we cannot make predictions about future or otherwise non-observed phenomena.

Inductive reasoning: conclusion merely likely Inductive reasoning begins with observations that are specific and limited in scope, and proceeds to a generalized conclusion that is likely, but not certain, in light of accumulated evidence. You could say that inductive reasoning moves from the specific to the general. Much scientific research is carried out by the inductive method: gathering evidence, seeking patterns, and forming a hypothesis or theory to explain what is seen.

Conclusions reached by the inductive method are not logical necessities; no amount of inductive evidence guarantees the conclusion. This is because there is no way to know that all the possible evidence has been gathered, and that there exists no further bit of unobserved evidence that might invalidate my hypothesis. Thus, while the newspapers might report the conclusions of scientific research as absolutes, scientific literature itself uses more cautious language, the language of inductively reached, probable conclusions:

What we have seen is the ability of these cells to feed the blood vessels of tumors and to heal the blood vessels surrounding wounds. The findings suggest that these adult stem cells may be an ideal source of cells for clinical therapy. For example, we can envision the use of these stem cells for therapies against cancer tumors [...].1

Because inductive conclusions are not logical necessities, inductive arguments are not simply true. Rather, they are cogent: that is, the evidence seems complete, relevant, and generally convincing, and the conclusion is therefore probably true. Nor are inductive arguments simply false; rather, they are not cogent .

It is an important difference from deductive reasoning that, while inductive reasoning cannot yield an absolutely certain conclusion, it can actually increase human knowledge (it is ampliative ). It can make predictions about future events or as-yet unobserved phenomena.

For example, Albert Einstein observed the movement of a pocket compass when he was five years old and became fascinated with the idea that something invisible in the space around the compass needle was causing it to move. This observation, combined with additional observations (of moving trains, for example) and the results of logical and mathematical tools (deduction), resulted in a rule that fit his observations and could predict events that were as yet unobserved.

Abductive reasoning: taking your best shot Abductive reasoning typically begins with an incomplete set of observations and proceeds to the likeliest possible explanation for the set. Abductive reasoning yields the kind of daily decision-making that does its best with the information at hand, which often is incomplete.

A medical diagnosis is an application of abductive reasoning: given this set of symptoms, what is the diagnosis that would best explain most of them? Likewise, when jurors hear evidence in a criminal case, they must consider whether the prosecution or the defense has the best explanation to cover all the points of evidence. While there may be no certainty about their verdict, since there may exist additional evidence that was not admitted in the case, they make their best guess based on what they know.

While cogent inductive reasoning requires that the evidence that might shed light on the subject be fairly complete, whether positive or negative, abductive reasoning is characterized by lack of completeness, either in the evidence, or in the explanation, or both. A patient may be unconscious or fail to report every symptom, for example, resulting in incomplete evidence, or a doctor may arrive at a diagnosis that fails to explain several of the symptoms. Still, he must reach the best diagnosis he can.

The abductive process can be creative, intuitive, even revolutionary.2 Einstein's work, for example, was not just inductive and deductive, but involved a creative leap of imagination and visualization that scarcely seemed warranted by the mere observation of moving trains and falling elevators. In fact, so much of Einstein's work was done as a "thought experiment" (for he never experimentally dropped elevators), that some of his peers discredited it as too fanciful. Nevertheless, he appears to have been right-until now his remarkable conclusions about space-time continue to be verified experientially.

References 1. Verfaillie, Catherine. "Adult Bone Marrow Stem Cells Can Become Blood Vessels." News release from the University of Minnesota. Jan. 30, 2002. June 1, 2005. < http://www.sciencedaily.com/releases/2002/01/020131074645.htm >

2. Thagard, Paul and Cameron Shelley. "Abductive reasoning: Logic, visual thinking, and coherence." Waterloo, Ontario: Philosophy Department, Univerisity of Waterloo, 1997. June 2, 2005. < http://cogsci.uwaterloo.ca/Articles/Pages/%7FAbductive.html >

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10: Deductive Reasoning

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• Page ID 22019

• Bradley H. Dowden
• California State University Sacramento

In any argument, the arguer intends the reasons to adequately support the conclusion, to imply it. For example, if you want people to conclude that your product is the best buy for them, you ought to give them some good reasons. What makes the reasons good enough is that they imply that your product is the best buy for them. This chapter explores how the notion of implication lies at the heart of logical reasoning. There are two kinds of implication that can be involved—deductive or inductive. This chapter focuses on deductive arguments, and the main goal of a deductive argument is to satisfy the standard of being deductively valid. We will define “deductive validity” very soon.

• 10.1: Implying with Certainty vs. with Probability
• 10.2: Distinguishing Deduction from Induction
• 10.3: Review of Major Points
• 10.4: Glossary
• 10.5: Exercises

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• Korean J Med Educ
• v.31(4); 2019 Dec

Reasoning processes in clinical reasoning: from the perspective of cognitive psychology

Hyoung seok shin.

Department of Medical Education, Korea University College of Medicine, Seoul, Korea

Clinical reasoning is considered a crucial concept in reaching medical decisions. This paper reviews the reasoning processes involved in clinical reasoning from the perspective of cognitive psychology. To properly use clinical reasoning, one requires not only domain knowledge but also structural knowledge, such as critical thinking skills. In this paper, two types of reasoning process required for critical thinking are discussed: inductive and deductive. Inductive and deductive reasoning processes have different features and are generally appropriate for different types of tasks. Numerous studies have suggested that experts tend to use inductive reasoning while novices tend to use deductive reasoning. However, even experts sometimes use deductive reasoning when facing challenging and unfamiliar problems. In clinical reasoning, expert physicians generally use inductive reasoning with a holistic viewpoint based on a full understanding of content knowledge in most cases. Such a problem-solving process appears as a type of recognition-primed decision making only in experienced physicians’ clinical reasoning. However, they also use deductive reasoning when distinct patterns of illness are not recognized. Therefore, medical schools should pursue problem-based learning by providing students with various opportunities to develop the critical thinking skills required for problem solving in a holistic manner.

Introduction

It is hard to describe clinical reasoning in a sentence, because it has been studied by a number of researchers from various perspectives, such as medical education, cognitive psychology, clinical psychology, and so forth, and they have failed to reach an agreement on its basic characteristics [ 1 ]. Accordingly, clinical reasoning has been defined in various ways. Some researchers defined clinical reasoning as a crucial skill or ability that all physicians should have for their clinical decision making, regardless of their area of expertise [ 2 , 3 ]. Others focused more on the processes of clinical reasoning; thus, they defined it as a complex process of identifying the clinical issues to propose a treatment plan [ 4 - 6 ]. However, these definitions are not so different. Taking this into account, it can be concluded that clinical reasoning is used to analyze patients’ status and arrive at a medical decision so that doctors can provide the proper medical treatment.

In reality, properly working clinical reasoning requires three domains of knowledge: diagnostic knowledge, etiological knowledge, and treatment knowledge [ 6 ]. From the perspective of cognitive psychology, structural knowledge is needed to integrate domain knowledge and find solutions based on the learner’s prior knowledge and experience [ 7 ], and structural knowledge can be constructed as a form of mental model by understanding the relations between the interconnected factors involved in clinical issues [ 8 , 9 ]. In this cognitive process, critical thinking skills such as causal reasoning and systems thinking can play a pivotal role in developing deeper understanding of given problem situations. Causal reasoning is the ability to identify causal relationships between sets of causes and effects [ 10 ]. Causality often involves a series or chain of events that can be used to infer or predict the effects and consequences of a particular cause [ 10 - 13 ]. Systems thinking is a thinking paradigm or conceptual framework where understanding is defined in terms of how well one is able to break a complex system down into its component parts [ 14 , 15 ]. It is based on the premise that a system involves causality between factors that are parts of the system as a whole [ 14 ]. Systems thinking is a process for achieving a deeper understanding of complex phenomena that are composed of components that are causally interrelated [ 14 - 16 ]. As a result, causal reasoning and systems thinking are skills that can help people to better understand complex phenomena in order to arrive at effective and targeted solutions that address the root causes of complex problems [ 10 , 12 , 15 ].

If cognitive skills work properly, one can make correct decisions all of the time. However, human reasoning is not always logical, and people often make mistakes in their reasoning. The more difficult the problems with which they are presented, the more likely they are to choose wrong answers that are produced by errors or flaws in the reasoning process [ 17 , 18 ]. Individual differences in reasoning skills—such as systems thinking, causal reasoning, and thinking processes—may influence and explain observed differences in their understanding. Therefore, to better assist learners in solving problems, instructors should focus more on facilitating the reasoning skills required to solve given problems successfully.

In this review paper, the author focuses on the reasoning processes involved in clinical reasoning, given that clinical reasoning is considered as a sort of problem-solving process. Therefore, this paper introduces concepts related to the reasoning processes involved in clinical reasoning and their influences on novices and experts in the field of medical education from the perspective of cognitive psychology. Then, based on the contents discussed, the author will be able to propose specific instructional strategies associated with reasoning processes to improve medical students’ reasoning skills to enhance their clinical reasoning.

Concepts and nature of reasoning processes

Generally, reasoning processes can be categorized into two types: inductive/forward and deductive/backward [ 19 ]. In an inductive reasoning process, one observes several individual facts first, then makes a conclusion about a premise or principle based on these facts. Yet there may be the possibility that a conclusion is not true even though a premise or principle in support of that conclusion is true, because the conclusion is generalized from the facts observed by the learner, but the learner does not observe all relevant examples [ 20 ].

In general, in a deductive reasoning process, according to Johnson-Laird [ 20 ], one establishes a mental model or a set of models to solve given problems considering general knowledge and principles based on a solid foundation. Then, one makes a conclusion or finds a solution based on the mental model or set of models. To verify a mental model, one needs to check the validity of the conclusions or solutions by searching for counterexamples. If one cannot find any counterexamples, the conclusions can be accepted as true and the solutions as valid. Consequently, the initial mental model or set of models can be used for deductive reasoning.

Anderson [ 17 ] proposed three different ways of solving complex problems: means-ends analysis, working backward, and planning by simplification. A means-ends analysis is a process that gets rid of differences between the current state and the ideal state in order to determine sub-goals in solving problems, and the process can be repeated until the major goal is achieved [ 21 - 23 ]. It can be considered an inductive reasoning process, because the distinct feature of means-ends analysis where it achieves sub-goals in consecutive order is similar to inductive reasoning. Working backward is addressed as an opposite concept to means-ends analysis [ 17 ], because it needs to set up a desired result to find causes by measuring the gap between the current state and the ideal state; then, this process is repeated until the root causes of a problem are identified. According to Anderson [ 17 ], means-ends analysis (inductive reasoning) is more useful in finding a solution quickly when a limited number of options are given or many sub-goals should be achieved for the major goal; whereas working backward (deductive reasoning) spends more time removing wrong answers or inferences to find the root causes of a problem. In conclusion, inductive and deductive reasoning processes have different features and can play different roles in solving complex problems.

The use of reasoning processes

A number of researchers across different fields have used inductive and deductive approaches as reasoning processes to solve complex problems or complete tasks. For example, Scavarda et al. [ 24 ] used both approaches in their study to collect qualitative data through interviews with experts, and they found that experts with a deductive approach used a top-down approach and those with an inductive approach used a bottom-up approach to solve a given problem. In a study of Overmars et al. [ 25 ], the results showed that a deductive approach explicitly illustrated causal relations and processes in 39 geographic contexts and it was appropriate for evaluating various possible scenarios; whereas an inductive approach presented associations that did not guarantee causality and was more useful for identifying relatively detailed changes.

Sharma et al. [ 26 ] found that inductive or deductive approaches can both be useful depending on the characteristics of the tasks and resources available to solve problems. An inductive approach is considered a data-driven approach, which is a way to find possible outcomes based on rules detected from undoubted facts [ 26 ]. Therefore, if there is a lot of available data and an output hypothesis, then it is effective to use an inductive approach to discover solutions or unexpected and interesting findings [ 26 , 27 ]. An inductive approach makes it possible to directly reach conclusions via thorough reasoning that involves the following procedures: (1) recognize, (2) select, and (3) act [ 28 ]. These procedures are recurrent, but one cannot know how long they should be continued to complete a task, because a goal is not specified [ 26 ]. Consequently, an inductive approach is useful when analyzing an unstructured data set or system [ 29 ].

On the other hand, a deductive approach sets up a desired goal first, then finds a supporting basis—such as information and rules—for the goals [ 26 ]. For this, a backward approach, which is considered deductive reasoning, gradually gets rid of things proved unnecessary for achieving the goal while reasoning; therefore, it is regarded as a goal-driven approach [ 28 ]. If the output hypothesis is limited and it is necessary to find supporting facts from data, then a deductive approach would be effective [ 26 , 28 ]. This implies that a deductive approach is more appropriate when a system or phenomenon is well-structured and relationships between the components are clearly present [ 29 ]. Table 1 shows a summary of the features and differences of the inductive and deductive reasoning processes.

Features of Inductive and Deductive Reasoning Processes

The classification according to the reasoning processes in the table is dichotomous, but they do not always follow this classification absolutely. This means that each reasoning process shows such tendencies.

Considering the attributes of the two reasoning processes, an inductive approach is effective for exploratory tasks that do not have distinct goals—for example, planning, design, process monitoring, and so on, while a deductive approach is more useful for diagnostic and classification tasks [ 26 ]. In addition, an inductive approach is more useful for discovering solutions from an unstructured system. On the other hand, a deductive approach can be better used to identify root causes in a well-structured context. While both reasoning approaches are useful in particular contexts, it can be suggested that inductive reasoning is more appropriate than deductive reasoning in clinical situations, which focus on diagnosis and treatment of diseases rather than on finding their causes.

Reasoning processes by novices and experts

As mentioned above, which reasoning process is more effective for reaching conclusions can be generally determined depending on the context and purpose of the problem solving. In reality, however, learners’ choices are not always consistent with this suggestion, because they are affected not only by the problem itself, but also by the learner. Assuming that learners or individuals can be categorized into two types, novices and experts, based on their level of prior knowledge and structural knowledge, much research has shown that novices and experts use a different reasoning process for problem solving. For example, in a study of Eseryel et al. [ 30 ], novice instructional designers who possessed theoretical knowledge but little experience showed different patterns of ill-structured problem solving compared to experts with real-life experience. Given that each learner has a different level of prior knowledge relating to particular topics and critical thinking skills, selecting the proper reasoning process for each problem is quite complex. This section focuses on which reasoning process an individual uses depending on their content and structural knowledge.

Numerous studies have examined which reasoning processes are used by experts, who have sufficient content and structural knowledge, and novices, who have little content and structural knowledge, for problem solving. The result of a study of Hong et al. [ 31 ] showed that children generally performed better when using cause-effect inferences (inductive approach) than effect-cause inferences (considered a deductive approach). According to Anderson [ 17 ], people are faced with some difficulties when they solve problems using induction. The first difficulty is in formulating proper hypotheses and the second is that people do not know how to interpret negative evidence when it is given and reach a conclusion based on that evidence [ 17 ]. Nevertheless, most students use a type of inductive reasoning to solve problems that they have not previously faced [ 32 ]. Taken together, the studies suggest that novices generally prefer an inductive approach to a deductive approach for solving problems because they may feel comfortable and natural using an inductive approach but tend to experience difficulties during problem-solving processes. From these findings, it can be concluded that novices are more likely to use inductive reasoning, but it is not always productive.

Nevertheless, there is still a controversy about which reasoning processes are used by experts or novices [ 33 ]. For example, experts in specific domains use an inductive approach to solving problems, but novices, who have a lower level of prior knowledge in specific domains, tend to use a deductive approach [ 23 ]. In contrast, according to Smith [ 34 ], studies in which more familiar problems were used concluded that experts preferred an inductive approach, whereas in studies that employed relatively unfamiliar problems that required more time and effort to solve, experts tended to prefer a deductive approach. In line with this finding, in solving physics problems, experts mostly used inductive reasoning that was faster and had fewer errors for problem solving only when they encountered easy or familiar problems where they could gain a full understanding of the situation quickly, but novices took more time to deductively reason by planning and solving each step in the process of problem solving [ 35 ].

Assuming that an individual’s prior knowledge consists of content knowledge such as knowledge of specific domains as well as structural knowledge such as the critical thinking skills required for problem solving in the relevant field, it seems experts use an inductive approach when faced with relatively easy or familiar problems; while a deductive approach is used for relatively challenging, unfamiliar, or complex problems. In the case of novices, it may be better to use deductive reasoning for problem solving considering that they have a lower level of prior knowledge and that even experts use deductive reasoning to solve complex problems.

Inductive and deductive reasoning in clinical reasoning

In medicine, concepts of inductive and deductive reasoning apply to gathering appropriate information and making a clinical diagnosis considering that the medical treatment process is a form of problem solving. Inductive reasoning is used to make a diagnosis by starting with an analysis of observed clinical data [ 36 , 37 ]. Inductive reasoning is considered as scheme-inductive problem solving in medicine [ 36 ], because in inductive reasoning, one first constructs his/her scheme (also considered a mental model) based on one’s experiences and knowledge. It is generally used for a clinical presentation-based model, which has been most recently applied to medical education [ 38 ].

In contrast, deductive reasoning entails making a clinical diagnosis by testing hypotheses based on systematically collected data [ 39 ]. Deductive reasoning is considered an information-gathering method, because one constructs a hypothesis first then finds supporting or refuting facts from data [ 36 , 40 ]. It has been mostly used for discipline-based, system-based, and case-based models in medical education [ 38 ].

Inductive and deductive reasoning by novice and expert physicians

A growing body of research explores which reasoning processes are mainly used by novices and experts in clinical reasoning. Novice physicians generally use deductive reasoning, because limited knowledge restricts them from using deductive reasoning [ 1 , 38 ]. Also, it is hard to consider deductive reasoning as an approach generally used by experts, since they do not repeatedly test a hypothesis based on limited knowledge in order to move on to the next stage in the process of problem solving [ 38 ]. Therefore, it seems that deductive reasoning is generally used by novices, while inductive reasoning is used by expert physicians in general. However, this may be too conclusive and needs to be further examined in the context of clinical reasoning.

In clinical reasoning, inductive reasoning is more intuitive and requires a holistic view based on a full understanding of content knowledge, including declarative and procedural knowledge, but also structural knowledge; thus, it occurs only when physicians’ knowledge structures of given problems are highly organized [ 38 ]. Expert physicians recognize particular patterns of symptoms through repeated application of deductive reasoning, and the pattern recognition process makes it possible for them to apply inductive reasoning when diagnosing patients [ 10 ]. As experts automate a number of cognitive sequences required for problem solving in their own fields [ 35 ], expert physicians automatically make appropriate diagnoses following a process of clinical reasoning when they encounter patients who have familiar or typical diseases. Such a process of problem solving is called recognition-primed decision making (RPDM) [ 41 , 42 ]. It is a process of finding appropriate solutions to ill-structured problems in a limited timeframe [ 10 ]. In RPDM, expert physicians are aware of what actions should be taken when faced with particular situations based on hundreds of prior experiences [ 10 ]. These prior experiences are called illness scripts in diagnostic medicine [ 10 ], and this is a concept similar to a mental model or schema in problem solving.

However, expert physicians do not always use inductive reasoning in their clinical reasoning. Jonassen [ 10 ] categorized RPDM into three forms of variations in problem solving by experts, and the first form of variation is the simplest and easiest one based on inductive reasoning, as mentioned above. The second type of variation occurs when an encountered problem is somewhat atypical [ 10 ]. Even expert physicians are not always faced with familiar or typical diseases when treating patients. Expert physicians’ RPDM does not work automatically when faced with atypical symptoms, because they do not have sufficient experiences relevant to the atypical symptoms. In this case, it can be said that they have weak illness scripts or mental models of the given symptoms. In the second variation, experts need more information and will attempt to connect it to their prior knowledge and experiences [ 10 ]. Deductive reasoning is involved in this process so that problem solvers can test their hypotheses in order to find new patterns and construct new mental models based on the newly collected data and previous experiences. The third variation of RPDM is when expert physicians have no previous experience or prior knowledge of given problem situations; in other words, no illness script or mental model [ 10 ]. Jonassen [ 10 ] argued that a mental simulation is conducted to predict the consequences of various actions by experts in the third variation. This process inevitably involves repetitive deductive reasoning to test a larger number of hypotheses when making a diagnosis.

Similarly, from the perspective of dual process theory as a decision-making process, decision making is classified into two approaches based on the reasoning style: type 1 and type 2 (or system 1 and system 2) [ 43 , 44 ]. According to Croskerry [ 44 ], the type 1 decision-making process is intuitive and based on experiential-inductive reasoning, while type 2 is an analytical and hypothetico-deductive decision-making process [ 44 , 45 ]. A feature that distinguishes the two processes is whether a physician who encounters a patient’s symptoms succeeds in pattern recognition. If a physician recognizes prominent features of the visual presentation of illness, type 1 processes (or system 1) are operated automatically, whereas type 2 (or system 2) processes work if any distinct feature of illness presentation is not recognized [ 44 ].

Only experienced expert physicians can use RPDM [ 10 , 46 ] or type 1 and 2 processes [ 43 ], because it can occur solely based on various experiences and a wide range of prior knowledge that can be gained as a result of a huge amount of deductive reasoning since they were novices. Consequently, it can be concluded that expert physicians generally use more inductive reasoning when they automatically recognize key patterns of given problems or symptoms, while sometimes they also use deductive reasoning when they additionally need processes of hypothesis testing to recognize new patterns of symptoms.

From the perspective of cognitive processes, clinical reasoning is considered as one of the decision-making processes that finds the best solutions to patients’ illnesses. As a form of decision making for problem solving, two reasoning processes have been considered: inductive and deductive reasoning. Deductive reasoning can be used to make a diagnosis if physicians have insufficient knowledge, sufficient time, and the ability to analyze the current status of their patients. However, in reality, it is inefficient to conduct thorough deductive reasoning at each stage of clinical reasoning because only a limited amount of time is allowed for both physicians and patients to reach a conclusion in most cases. A few researchers have suggested that using deductive reasoning is more likely to result in diagnostic errors than inductive reasoning, because evidence-based research, such as deductive reasoning, focuses mainly on available and observable evidence and rules out the possibility of any other possible factors influencing the patient’s symptoms [ 37 , 38 ]. However, when a physician encounters unfamiliar symptom and the degree of uncertainty is high, deductive reasoning is required to reach the correct diagnosis through analytical and slow diagnostic processes by collecting data from resources [ 44 ]. Taken together, in order to make the most of a limited timeframe and reduce diagnostic errors, physicians should be encouraged to use inductive reasoning in their clinical reasoning as far as possible given that patterns of illness presentation are recognized.

Unfortunately, it is not always easy for novice physicians to apply inductive or deductive reasoning in all cases. Expert physicians have sufficient capabilities to use both inductive and deductive reasoning and can also automate their clinical reasoning based on inductive reasoning, because they have already gathered the wide range of experiences and knowledge required to diagnose various symptoms. Novice physicians should make a greater effort to use inductive reasoning when making diagnoses; however, it takes experiencing countless deductive reasoning processes to structure various illness scripts or strong mental models until they reach a professional level. As a result, teaching not only clinical reasoning as a whole process but also the critical thinking skills required for clinical reasoning is important in medical schools [ 47 ]. For this, medical schools should pursue problem-based learning by providing students with various opportunities to gain content knowledge as well as develop the critical thinking skills —such as data analysis skills, metacognitive skills, causal reasoning, systems thinking, and so forth—required for problem solving in a holistic manner so that they can improve their reasoning skills and freely use both inductive and deductive approaches in any context. Further studies will be reviewed to provide detailed guidelines or teaching tips on how to develop medical students’ critical thinking skills.

Acknowledgments

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Author contributions

All work was done by HS.

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34 Strategy: Inductive and Deductive Reasoning

Strategy: inductive and deductive reasoning.

Two Ways of Understanding

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

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

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

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

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

The Power of Inductive Reasoning

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

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

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

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

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

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

Conclusion: Likely I am allergic to strawberries.

Inductive reasoning can never lead to absolute certainty. Instead, induction allows you to say that, given the examples provided for support, the claim more likely than not is true. Because of the limitations of inductive reasoning, a conclusion will be more credible if multiple lines of reasoning are presented in its support.

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

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

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

The Power of Deductive Reasoning

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

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

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

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

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

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

Evaluating the Truth of a Premise

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

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

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

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

Methods to Evaluate the Truth of a Premise

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

• the relevance of the additional data, and

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

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

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

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Built-In Practice: Inductive and Deductive Reasoning

Use these common terms in a sentence then create Inductive and Deductive reasoning for each.

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

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

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

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

Deductive Reasoning

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

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

Inductive Reasoning

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

Abductive Reasoning

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

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

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An ethical approach to a better life, by integrating desires and avoiding dogmatic extremes, critical thinking 2: induction and deduction.

There was a great response to my first post in this series, so thanks to everyone who contributed to that. For my second one I’ve decided to tackle an issue that’s quite crucial to how Critical Thinking relates to the Middle Way.

There are two types of argument, normally known as induction and deduction. Deductive argument is what is classically formulated as argument and abstracted into logic. Deductive argument begins with assumed claims known as premises, and draws a conclusion from those premises. Within the terms of interpretation and the beliefs it assumes, the logical link between the premises and conclusion in deductive argument is absolute. If the premises are correct, then the conclusion must be correct if the reasoning is valid (i.e. follows the laws of logic).

For example:

All engineers are human beings.

So John must be a human being.

This valid argument must be correct if John is indeed an engineer, and if indeed all engineers are human beings. But I might have false information about John, and robotic engineers may be under development right now in Japanese laboratories. My assumptions may be false, but if they happen to be true then the conclusion must also be true.

That’s what we’re traditionally told about deductive argument. However, if you take into account embodied meaning, then we are all also going to have slightly different meanings for ‘John’, ‘engineer’, ‘human being’ etc. experienced in different bodies. It’s only if we further assume that these meanings are sufficiently shared and stable that a deductive argument can be ‘valid’ in this way.

The other type of argument, however, is far more common and far more useful. Inductive argument is imperfect argument that begins with limited information and draws further conclusions from it. Sometimes such reasoning can sometimes be grossly over-stated and prejudiced. For example:

My neighbour was burgled by a man from Norfolk.

Norfolk people are all criminals.

This is a crude example of prejudiced reasoning. It takes just one example and draws a conclusion about a whole class of people that are extremely varied. It’s obviously not taking into account all the reasons why it might be wrong.

However, inductive reasoning is what we rely upon constantly. If we take into account its limitations it can be a justified way of reasoning. For example:

Tom has failed to fulfil his promises to his mother on ten successive occasions within a month.

His mother should not place any further reliance on Tom’s promises.

It’s still possible that Tom is trustworthy, and has just had ten unfortunate sets of events that stopped him fulfilling his promises, or that he was at fault in the past but has now completely reformed. However, it’s unlikely. We are usually obliged to trust the weight of our experience in cases like this. We could have confidence in the conclusion here as long as we also took into account the possibility that we could be wrong.

So, my argument here is that deductive argument, though perfect in theory, is not really different from inductive in the way we should treat it in practice. Deductive logic, though it may be perfect in the abstract, is in practice dependent both on the assumptions made and on the interpretation of the words of the argument to be applied in our lives. In practice, then deductive reasoning is just as fallible as inductive. If we use inductive reasoning with awareness of its limitations, though, it can provide us with justified beliefs. This kind of reasoning is justified because of its fallibility, not in spite of it.

How well justified are the following (inductive or deductive) arguments?

1. The no. 37 bus that I take to work has been more than ten minutes late on three successive days. This is an unacceptable standard of punctuality, and it has made me late for work. I shall write to the bus company to complain.

2. If the UK economy continues to grow at the rate it managed during the last quarter of 2013 (a rate unmatched since the crash of 2008) then we can conclude that economic recovery is well under way.

3. Romanians in the UK are arrested at seven times the rate of Britons ( Daily Mail ), so if more Romanians come to the UK we can expect an increase in crime.

4. If God exists and God is good, he would not allow people to suffer without making the truth available to them. So he must have sent divinely-inspired prophets to guide people.

5. I’ve been practising this difficult piano piece for two months now, but I seem to be making no progress. I keep making the same mistakes. I should give up this piece and learn something else.

Picture: Engineer at work by Angelsman (Wikimedia Commons)

5 thoughts on “ Critical Thinking 2: Induction and deduction ”

I have made an attempt to answer the questions. 1. An inductive argument, not well justified. 2. Also inductive reasoning, probably justified. 3. Deductive logic (an absolute claim) not well justified. No matter what theorythe Daily Mail supports 4. Also deductive and not well jusified. 5. Inductive argument which is not well jsutified, hard peices take longer to learn. I’ll post this now, but will have another think about my answers.

I’m going to rate the justification of each argument from 0 (not justified at all) to 10 (as justifiable as is possible).

1. Rate = 4. This seems fairly justified, but three times in a row does not seem excessive and might not indicate a systemic problem with the bus company. It is quite probable that three separate and unavoidable random events took place, causing the bus to be late each time.

2. Rate = 6. I found this difficult. The argument only states that the economy continues to grow, but not for how long. It also says that the recovery is well under way, rather than just under way. Either way, since it has not grown as much since 2008, if it continues to then it is reasonable to claim that a recovery is well under way.

3. Rate = 4. This one is challenging. It does not take into account the possibility of discrimination by the police, or the fact that Romanians might not be as good as evading arrest. However, I can understand how – presented as it is – this conclusion can be reached.

4. Rate = 1. This one scores low because the conclusion seems quite wild – it assumes that revelation of the truth can only be achieved by God, via prophets.

5. Rate = 3. It would be reasonable to wish to give up but making the same mistake for two months does not mean that you will continue to make this mistake, and the best way to enable oneself to overcome this difficulty is to practice.

🙁 I feel a bit upset that I have given my highest score to the daily mail example, but I suppose this is an emotional, rather than logical response.

Hi Robert, 1. Using Rich’s 1-10 ratings I would say this is fairly justified: Rating 6 out of 10. There are arguably more mitigating circumstance for a bus to be late than say a train, such as heavy early morning traffic, passengers searching for change while paying etc. One should also take into account to what degree the bus service guarantees punctuality. However, given the fact that the person uses the present simple tense to express habitual action (the bus I take to work), I feel one can make the reasonable assumption that the person normally relies on this service rather than other forms of transport to get them to work on time and that reliability has to be backed up by a certain degree of confidence (presumably base on experience) in the punctuality of the service, otherwise they would choose another mode of transport. My degree of justification in this example has also been influenced by the fairly punctual timetable that buses operate to in my neck of the woods.

2. I found this a difficult one as I don’t have a good grasp of economics and to what degree of confidence one can have in an upward economic trend continuing and for what period. You’ve mentioned that Nicholas Taleb in his book “Antifragile” suggests that , economists and market analysts often put far too much faith in their economic predictions that don’t take sufficient account of the complexity of market forces and other unpredictable influencing factors . Also, why should it be a given that the UK will remain one of the top world economies, especially if you take into account the rise of economies such as India, Brazil, Russia etc. So I think I’ll giving this a rating of 4.

3. At first glance this argument appears strong in a deductive sense. However, one would need to question what these Romanians were arrested for eg. maybe for being illegal immigrants? – which now would be no longer the case, given the new EU Law. Also, what size of control group did they use, 7 people out of 10, or 70,000 out of 100,000 and where did they take that control group from and what deciding factors did they use? Arguably, in any argument as well, one needs to take into account to some extent not just what is being judged, but also the judger. Does the Daily Mail place great importance in providing their readers with a balanced view when putting forward an argument? Would the Daily Mail possibly have a vested interest in wanting their readers to feel that 70% of Romanians living this country are criminals? Is this me employing an Ad Hominim argument though? Rating 2 out of 10

4. Another example of a seductive deductive argument, if the premises are true then the conclusion must be true. However, I think what’s important is how well the premises stand up. It’s a bit like saying, If all polar bears could play the piano, and were classically trained then at some stage in the training they would most likely learn “Fur Elise” by Beethoven. I think I can say with a strong degree of justification that there is no way of knowing whether God exists or not (given that concept is not subject to experience), so any hypothesizing about it is arguably irrelevant (unless one is playing safe of course). Rating 0 out of 10

5. I feel this is fairly well justified and give it a rating of 7 out of 10. Admittedly, the piano player could conceivably be giving up too early as Norma and Rich point out . However, given the fact that they are attempting a difficult piece (‘difficult’ is also relative of course), one could make the reasonable assumption that they are fairly proficient and would not have reached that degree of proficiency without plenty of practice and some experience of when to accept that some pieces at their stage of learning are too challenging (and therefore potentially demotivating) for them. I feel it is quite reasonable to assume that they have reached that point here and are making a pragmatic decision to move on.

‘I feel a bit upset that I have given my highest score to the daily mail example, but I suppose this is an emotional, rather than logical response’

Interestingly, this is a false statement. I believed it at the time of writing my last post (above), but having read my answer again I can see that I gave a rating of 6 to the economy argument and only 4 to the daily mail argument. It is amazing how quickly a false memory can occur. Giving a 4 to the daily mail argument was enough to disturb me into this error!!

Whether the arguments are inductive or deductive is rather less open to argument here than how well justified they are! Part of the point of the exercise, though, is to think about the different ways that inductive and deductive arguments are justified. These are my answers:

1. This is an inductive argument, generalising from three occasions. If you compare it with generalisation from three examples elsewhere, it becomes clearer how weak this is. For example, if I went to a business conference and met three Russian businessmen, and concluded from this experience that all Russians were businessmen. I think this argument is weaker than Rich or Barry are recognising.

2. This is a deductive argument. It is entirely hypothetical, stating that *if* one thing is the case then the other must be. The two things are “the economy continues to grow at the same rate it managed in the last quarter of 2013” and “economic recovery is well under way”. This seems to me obviously the case. No assumptions are being made about whether the recovery will continue – the hypothetical event is just being re-described. This can be seen as a valid deductive argument, regardless of whether you know anything about economics, and regardless of whether you agree with the hypothesis.

3. You could interpret this either inductively or deductively. Inductively, it would be arguing from a higher rate of arrests amongst Romanians already in the UK to an overall increase in crime if further Romanians arrive in future. If you take it this way it is weak, because the new Romanians might be quite different from the current Romanians. The rate of arrests amongst current Romanians compared to the rest of the population also tells you nothing about how many arrests have been made (there might be a very small number), so even if it was true (as no doubt many Daily Mail readers assume) that Romanians in general are 7 times more inclined to criminality than average, the impact on crime rates in general might still be negligible because the number of Romanians is very small when compared to the numbers in the general population.

If you take this as a deductive argument, it’s even worse. It certainly doesn’t follow from the arrest rates of current Romanians that there will necessarily be an increase in crime of future Romanians.

4. This is a deductive argument that seems entirely valid. As Barry points out, however, it’s only of any relevance if you accept the assumptions it begins with.

5. This is inductive. I was interested in the variety of responses this got, as this is a real dilemma that I have encountered as an amateur pianist! How strong it is depends on other assumptions, I think, such as how much you really want to learn the piece, particularly when compared to others that you could more realistically learn. In general (on the basis of individual experience) I’m inclined to agree with Barry that it’s quite a strong argument (though not overwhelmingly so). If I’ve made little progress with a piece in two months then I may be ‘flogging a dead horse’ to continue.

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Critical thinking skills, what is inductive reasoning.

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Inductive reasoning: conclusion merely likely Inductive reasoning begins with observations that are specific and limited in scope, and proceeds to a generalized conclusion that is likely, but not certain, in light of accumulated evidence. You could say that inductive reasoning moves from the specific to the general. Much scientific research is carried out by the inductive method: gathering evidence, seeking patterns, and forming a hypothesis or theory to explain what is seen.

Conclusions reached by the inductive method are not logical necessities; no amount of inductive evidence guarantees the conclusion. This is because there is no way to know that all the possible evidence has been gathered, and that there exists no further bit of unobserved evidence that might invalidate my hypothesis. Thus, while the newspapers might report the conclusions of scientific research as absolutes, scientific literature itself uses more cautious language, the language of inductively reached, probable conclusions:

What we have seen is the ability of these cells to feed the blood vessels of tumors and to heal the blood vessels surrounding wounds. The findings suggest that these adult stem cells may be an ideal source of cells for clinical therapy. For example, we can envision the use of these stem cells for therapies against cancer tumors [...].

Because inductive conclusions are not logical necessities, inductive arguments are not simply true. Rather, they are cogent: that is, the evidence seems complete, relevant, and generally convincing, and the conclusion is therefore probably true. Nor are inductive arguments simply false; rather, they are  not cogent .

It is an important difference from deductive reasoning that, while inductive reasoning cannot yield an absolutely certain conclusion, it can actually increase human knowledge (it is  ampliative ). It can make predictions about future events or as-yet unobserved phenomena.

For example, Albert Einstein observed the movement of a pocket compass when he was five years old and became fascinated with the idea that something invisible in the space around the compass needle was causing it to move. This observation, combined with additional observations (of moving trains, for example) and the results of logical and mathematical tools (deduction), resulted in a rule that fit his observations and could predict events that were as yet unobserved.

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• Last Updated: Jul 28, 2020 5:53 PM
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