What is inductive reasoning? Definition, examples, and application (2025)

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David Burbridge

May 27, 2024

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What is inductive reasoning? Definition, examples, and application (2)

Everyone values reason and rationality, but what does applying reason look like? The term “reason,” as it is used in fields like philosophy, usually refers to our ability to use logic to consciously draw conclusions from the information we have. We often refer to two kinds of reasoning: Inductive and deductive.

When we draw general conclusions based on specific observations, we usually call this inductive reasoning. In contrast, deductive reasoning moves from general premises to specific conclusions. In short, inductive reasoning is when we use specific instances to derive general principles or patterns and then apply these patterns to make predictions and form hypotheses.

Understanding inductive reasoning

Inductive reasoning is like putting together the pieces of a jigsaw puzzle to create a picture you have never seen before. You might not know what the result will be, but by taking logical steps to fit things together, you can gradually predict the result with increasing accuracy. Compared to deductive reasoning, which aims for certainty, inductive reasoning deals with probability and likelihood.

Examples of inductive reasoning

Rather than telling you what inductive reasoning is, let’s look at some examples to help you grasp the concept better.

Example 1

Every year, you get a stuffy nose and itchy eyes in early spring—classic allergy symptoms. You notice this happening every time spring rolls around, but not at other times of the year. From this observation, you suppose that you are allergic to something in the air in early spring. You could then look up known information related to this observation (cedar pollen is a common allergen in early spring), or use your observations (there are a lot of cedar trees near my house) to create the hypothesis that you are allergic to cedar pollen. This is an example of inductive reasoning because you’re inferring a general principle based on observations of specific instances to create a rule, then using knowledge to form a testable hypothesis.

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Example 2

Your pet cat sometimes makes a lot of noise late at night. When you check on the cat, you notice their food dish is empty, so you refill the food dish and notice that their meowing has stopped (for now!) After repeating this a few times, you generalize that the late night meowing is your cat’s attempt to communicate that it is hungry. From this consistent observation of your cat’s behavior and its response to you, you generalize that the cat meows when it is hungry. This is another instance of inductive reasoning, as you’re drawing a general rule from specific instances.

Specific inductive approaches

Bayesian probability

Bayesian probability, named for Thomas Bayes, is a mathematical method for calculating probability, even when we’re not entirely sure of what outcomes to expect. You start with an initial guess, which is the “prior probability,” then gather more information to help you update your guess based on this new evidence.

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This process isn’t a one-time thing; it’s ongoing. Every time you get new information, you tweak your belief a little more. Bayesian probability is used in many areas, including weather modeling and predicting disease prognoses, because it gives a structured way to deal with uncertainty.

Inductive logic programming (ILP)

ILP is a subfield of machine learning and artificial intelligence that let us induce general rules or hypotheses from specific instances or examples. In essence, it’s a way of getting a computer to apply inductive processes for us.

In ILP, a computer program receives a set of observed instances and corresponding outcomes, and it tries to infer general rules or patterns from these examples. Over time, the program iteratively refines its hypotheses based on the observed data, similar to the way Bayesian probability updates beliefs based on evidence.

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ILP is particularly useful in areas where explicit rules are difficult to define but where there is abundant data available for learning, such as in natural language processing and bioinformatics.

Benefits of an inductive approach

Inductive reasoning is a vital skill, and perhaps one of the most incredible innate faculties that we have. It’s widely used in scientific research, data analysis, problem-solving, and even everyday decision-making. It allows us to make educated guesses, form hypotheses, and uncover new insights even when we don’t have a lot to go on. Using an inductive approach can be open-ended and even fun. It encourages curiosity and exploration, driving innovation and discovery.

Conclusion

In conclusion, inductive reasoning is a powerful cognitive tool for drawing general conclusions from specific observations or evidence. By recognizing patterns and making educated guesses, we can uncover new knowledge and solve complex problems. Understanding the principles of inductive reasoning can enhance critical thinking skills and improve decision-making abilities in various domains of life.

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What is inductive reasoning? Definition, examples, and application (2025)

FAQs

What is inductive reasoning? Definition, examples, and application? ›

When you use a specific set of data or existing knowledge from past experiences to make decisions, you're using inductive reasoning. For example, if you review the population information of a city for the past 15 years, you may observe a consistent rate of population increase.

What is inductive reasoning and its examples? ›

Inductive reasoning is when you start with specific observations or facts, and infer a general rule or conclusion from them. For example, if you notice that every time you eat spicy food, you get a stomach ache, you might use inductive reasoning to conclude that spicy food causes stomach aches.

What are inductive reasoning applications? ›

Generally, we subconsciously make decisions based on our past observations and experiences. For example, if you leave for work and it's raining outside, you reasonably assume that it will rain the whole way and decide to carry an umbrella. This decision is an example of inductive reasoning.

What is inductive application? ›

In short, inductive reasoning is when we use specific instances to derive general principles or patterns and then apply these patterns to make predictions and form hypotheses.

What are 5 examples of induction? ›

Inductive reasoning examples
  • All cats I have seen are white. So, most of the cats are probably white.
  • Three cats out of 10 in the U.S. are white. So about 30% of cats in the U.S. are white.
  • Jen just moved here from Thailand. ...
  • Mei has new shoes. ...
  • The population of Thailand has risen steadily over the past 20 years.
Nov 1, 2020

What is an example of inductive reasoning in teaching? ›

Example of inductive teaching method

In a science class, students are presented with various leaves collected from different plants. They are asked to carefully observe the leaves and identify any patterns or similarities they notice.

What is a real life example of deductive reasoning? ›

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.

Which of the following is an example of inductive reasoning? ›

D. It has snowed every day this week, so it will likely snow tomorrow. The example of inductive reasoning is D. Inductive reasoning is a type of reasoning in which specific observations are used to make a general conclusion.

What is the application of inductive effect with example? ›

The inductive effect can be used to determine the stability of a molecule depending on the charge present on the atom and the groups bonded to the atom. For example, if an atom has a positive charge and is attached to a -I group its charge becomes 'amplified' and the molecule becomes more unstable.

What is inductive in simple words? ›

Inductive is an adjective form of the verb induct, meaning to bring about or bring in. Simply put, inductive reasoning involves using specific observations, evidence, or patterns to make a broad conclusion. You first collect evidence or make observations and then form an answer or theory based on what you have found.

What is inductive reasoning for dummies? ›

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It's usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions. Inductive reasoning is also called inductive logic or bottom-up reasoning.

What is an inductive argument example? ›

The following example illustrates how an inductive argument uses specific facts to make a broader conclusion: Premise: All the tigers I saw on my safari trip to South Africa were orange. Conclusion: Therefore, all tigers are orange.

What is inductive vs deductive reasoning? ›

Inductive reasoning involves starting from specific premises and forming a general conclusion, while deductive reasoning involves using general premises to form a specific conclusion. Conclusions reached via deductive reasoning cannot be incorrect if the premises are true.

What is an example of an inductive argument? ›

The following example illustrates how an inductive argument uses specific facts to make a broader conclusion: Premise: All the tigers I saw on my safari trip to South Africa were orange. Conclusion: Therefore, all tigers are orange.

Which of these is an example of inductive reasoning? ›

The example of inductive reasoning is: "Hundreds of individuals of a species have been observed and all are photosynthetic; therefore, the species is photosynthetic."Explanation: Inductive reasoning is the method of reasoning in which the premises of an argument support the conclusion but do not ensure it.

What is an example of an induction? ›

Induction is one of the three basic types of reasoning. Inductive arguments are those in which a conclusion is drawn about a class of objects, based upon the characteristics observed in a sample. For example, "Most of the jellybeans in my hand are red, and all of the jellybeans in my hand were taken from this jar.

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