What Is Inductive Reasoning? (With Types and Examples)

By Indeed Editorial Team

Updated 27 July 2022

Published 6 June 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Inductive reasoning is the process of making a logical generalisation based on your past experiences and observations. It is a type of analytical soft skill that helps you make better decisions. Employers often look for individuals who can use this reasoning to solve problems in the workplace and develop strategies based on certain patterns. In this article, we define inductive reasoning, describe its types and explain its process, along with examples.

What Is Inductive Reasoning?

It is the process of reaching a conclusion based on observations and experiences drawn from specific past instances. The past observations act as premises that supply a reasonably good assurance that the decision you are making is right in the given situation. For example, if you are about to go out and the sky looks cloudy, you may decide to take an umbrella with you. Here, your past observations guided your thought process to make a reasonable assumption that it may rain because the sky is cloudy.

Inductive reasoning is an essential tool for identifying patterns and making decisions. When you make an estimate based on the past trend displayed by a set of data, it is an instance of using inductive reasoning. For example, if you are required to predict the population growth rate of a city in the next five years, you may review the population growth data for the past 10-15 years and look for a trend to make an estimate.

It usually generalises the principles derived from specific instances, although it may not always be the case.

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Inductive Reasoning Examples

The chances are that you have used this method of decision-making at some point in time, although without being aware of it theoretically. Below are some examples of using inductive reasoning in a professional setup:

  • A recruiter analyses the qualities of his employees in the finance department and finds that most of the successful employees are either chartered accountants (CA) or chartered financial analysts (CFA) by qualification. He decides to focus his future recruitment efforts on candidates with these qualifications.

  • A digital marketer notices that customer queries increase substantially, leading to a higher conversion rate when they prominently display testimonials from past and existing customers on their company's website. Now they decide to place customer testimonials on all the product websites of the company.

  • A customer service representative realises that giving a patient listening to an irate customer substantially reduces their anger. He further notices that giving an assurance to look into their issue personally makes them happy and hopeful of the resolution. He starts following this process of patient listening and personal assurance while dealing with disgruntled customers.

  • A primary teacher observes that conducting fun activities like singing, dancing, painting and quizzing promotes creativity in the children. She decides to include these activities in her regular classroom sessions.

  • A painting contractor wins a contract to paint a building for Rs.1,50,000. He incurs a total cost of Rs.1,30,000 against his expected cost of Rs.90,000. Next time, he quotes a price of Rs.1,90,000 for a similar building.

  • A stock trader realises that the stock price of a petroleum products company fluctuates depending on the price of crude oil in the international market. He starts tracking the crude oil price to predict the price of the company's shares.

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Types Of Inductive Reasoning

There are three major types of inductive reasoning – inductive generalisation, analogical inference and causal inference.

Inductive generalisation

An inductive generalisation draws a conclusion about the broader population based on the trend or characteristics of the sample items collected from it. For example, a data digitisation company needs to determine the accuracy of 1000 pages of typing. It randomly picks 20 pages and finds that the average accuracy is 95%. It then tags all the 1,000 pages to be 95% accurate.

We can further categorise inductive generalisation into the following types:

  • Statistical generalisation: In this type of reasoning, we represent the sample using statistical data to draw conclusions about the population. For example, 95% of the employees from Team A achieved their sales target last month. Anil is in Team A. Anil is likely to have achieved his sales target last month.

  • Anecdotal generalisation: In this method, we draw a conclusion based on anecdotal evidence. For example, if you see a certain breed of a dog attacking someone on several occasions, you may generalise that such a breed is more aggressive than others.

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Analogical inference

In analogical inference, we compare the properties of two or more items. If they have some common properties, we infer that they also have some other properties in common. This type of reasoning is more popular in science, law and philosophy. For example, Mineral X and Mineral Y have similar boiling points and are commonly found in places of volcanic activity. Further, Mineral X is a good conductor of heat. Therefore, Mineral Y is also likely to be a good conductor.

Causal inference

In this type of reasoning, we infer a connection between two things or events based on the conditions of their occurrence. But, we are required to confirm some additional factors to establish the exact type of relationship. For example, someone broke into a house. The lock on the door was missing. Police pick up a stranger from the nearby area. The stranger had a similar lock in his bag. Police infer that the stranger broke into the house.

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Enumerative Induction Vs. Eliminative Induction

In enumerative induction, we draw a conclusion based on the number of supporting instances. A higher number of supporting instances makes the conclusion stronger. For example, all the living things found so far on earth are made up of cells. Hence, scientists may conclude that the life forms existing on some other planet would also be made up of cells.

In eliminative induction, we draw a conclusion based on the variety of supporting instances instead of the number of instances. A wider variety of supporting instances makes the conclusion stronger. For example, to find the underlying causes of lung cancer, researchers may study patients with different backgrounds, lifestyles and medical histories. If smoking is common in all the categories of patients, they may conclude that smoking causes lung cancer.

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The Inductive Reasoning Process

Inductive reasoning usually involves the following steps:

1. Collecting a representative sample

If you require drawing a conclusion regarding something, you first require to collect the sample data. The sample is required to be random to avoid any bias. Also, ensure that the sample is representative of the population. You can do this by including items from different categories in your sample. A larger and varied sample can give you higher accuracy.

2. Studying the sample

Study or observe the sample as closely as possible. Pay attention to details. Note down the features and characteristics of the sample that you notice. It is also essential to note down the number of sample items alongside the characteristics they display. For example, if out of a sample size of 20 employees, five have achieved 100% target and eight have achieved 90%, you can note down five alongside 100% target and eight alongside 90% target.

3. Recognising patterns

Look for a pattern or trend in the characteristics of the sample. Depending on the complexity of the study, you may be required to use various statistical and computational tools for this. For example, you may use a candlestick chart and technical indicators to analyse stock price patterns.

4. Making projections

This is the final step of drawing a conclusion. Try to find out how the results or events may unfold based on the behaviour of the sample. For example, you can project the past sales data to estimate future sales.

Inductive Vs. Deductive Reasoning

Inductive reasoning involves making generalised conclusions based on specific scenarios. For example, if you observe that the traffic on your way to the office is usually high at around 9 AM, you may decide to leave home at around 8 AM.

Deductive reasoning starts with a generalisation and tests the hypothesis by applying it to specific scenarios. For example, you may have a general perception that military personnel are honest. You then happen to meet a few retired officers on different occasions during your journey. You find that the military officers you met were, in fact, honest. This establishes your broad belief about all military personnel.

It seeks to predict a likely result, while the goal of deductive reasoning is to establish a hypothesis. If the hypothesis is correct, the conclusion drawn from a deductive argument is also correct. But, in the case of inductive reasoning, the conclusion is at most probable. While both types of reasoning are valuable, employers may be more interested in your inductive reasoning skill because it displays your aptitude for problem-solving in a work environment. Deductive reasoning finds its applications mostly in scientific researches.

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