Inductive Reasoning: What Is It? (With Examples)

If you read through a few job descriptions, you’ll probably run into the term “inductive reasoning.” While this sounds like a high-level logic model, it’s actually something that you use every day to solve problems and make observations about the world around you.

In this article, you’ll learn about what inductive reasoning is and read some examples of it. You’ll also learn how to apply, improve, and showcase your inductive reasoning skills as well as understand the differences between inductive, deductive, and abductive reasoning.

Key Takeaways

• Inductive reasoning uses specific observations and experiences to make broader statements.

• Inductive reasoning helps you make predictions, find trends, and come up with solutions.

• Inductive reasoning has its limitations because it’s often using a small amount of data and can be biased and personal.

• You can showcase your inductive reasoning skills in your resume, cover letter, and interview by showing examples of how you’ve used them.

What Is Inductive Reasoning?

Inductive reasoning is a way of thinking logically to make broad statements based on observations and experiences.

Going from the specific to the general is at the core of inductive logic. Anytime you make a bigger picture generalization, it’s inductive reasoning. The catch with inductive reasoning is that it’s not fool-proof. Like any guessing logic, there’s a chance that your generalization is incorrect, but that doesn’t mean that inductive assumptions aren’t useful.

Scientists use inductive reasoning all of the time to create hypotheses and test them with experiments. Without that generalized assumption based on a few observations, they wouldn’t even have a hypothesis to test.

Creating these general conclusions is a great way to understand the world around you and start to make educated decisions, from simple tasks like where to eat, all the way to which company would make a better partner.

Inductive reasoning follows this basic format — if you have four identical items and you know that three of them are orange, you can assume that the fourth is also orange. You’re going from a fact about a small group (three objects are orange) to a broader generalization that isn’t necessarily true (all four objects are orange), but likely is.

Examples of Inductive Reasoning

We use inductive reasoning in our everyday lives all the time, but the concept can be tricky to understand. Let’s look at a few daily and professional examples of inductive reasoning to better understand it.

In Daily Life

• You’re looking at flowers in your garden and you know that your roses bloom every year, so you can assume that they’ll bloom again this year.

• You notice that the dogs on your street bark at the mailman when he delivers mail, so you assume that all dogs bark at mailmen.

  (Note that this is an example of faulty inductive reasoning since only a few dogs were observed and it might not hold true that all dogs bark atall mailmen).

• You’re talking to your friends and you’re all telling stories about your grandparents. You guess that all people have grandparents based on your stories.

• You’re eating a bag of colored candy and the first four pieces you eat are blue and you guess that the fifth piece will also be blue.

At Work

• You need a weekly report from your coworker Mary before you can leave for the weekend. You know that she always sends you the report on Fridays between 2:30 and 3:30 pm before leaving. You can guess that Mary will also send you this week’s report on Friday between 2:30 and 3:30 pm.

• You ran a report and found out that 90% of the sales associates at your company signed a deal this month, so if you talk to your coworker John, a sales associate, you can guess that he signed a deal this month.

• You’re reading reviews of your company online and you notice that a customer has complained about how the company did not send a shipping tracking number once their items shipped. You assume that other customers also had this complaint and decide to add shipping tracking numbers to customers’ email receipts to remove this issue for future customers and make them happier.

• You work in HR and you notice that most of the top-performing employees you hired attended a certain university, so you decide to target that university more for recruiting efforts.

How Does Inductive Reasoning Work?

Inductive reasoning starts when you make an observation and then create some kind of generalization based on what you observed. Since the assumption is based on observation and examples, there can be truth in your reasoning.

Inductive reasoning can be especially helpful when you’re trying to make predictions or find trends. In both cases, you’re making a conclusion based on an observation of what has happened. Of course, your reasoning needs to be backed up by credible data in order to reach a rational conclusion, but using this logic can usually get you a good understanding of what’s going on.

Imagine you need to boost sales for your company’s online store. You notice that a product that has customer reviews on the page sells more units than a product without customer reviews.

The assumption you make is that products will sell better when there are customer reviews on the page, so you suggest to your boss to implement customer reviews on each product page to boost sales. This initial assumption may hold true, and looking at more products with and without customer reviews can help validate that generalized conclusion.

Types of Inductive Reasoning

• Inductive generalization. Many of the examples we’ve looked at so far are examples of generalization. Generalization means that you assume something about a general population based on a sample.

  For instance, if you drew 2 white balls and 1 black ball from a bag with 30 balls, inductive generalization would lead you to guess that the bag contains 20 white balls and 10 black balls.

• Statistical generalization. Statistical generalization requires a larger, more randomized sample to make strong inductive arguments. When you hear about political polling, there’s a lot of statistical generalizaiton going on. That’s why high-authority polls both use as large and diverse a population as possible, while also publishing the margin of error for any data presented.

  Think of statistical generalization as a more robust form of of inductive reasoning that applies principles from mathematics to arrive at more solid conclusions.

• Anecdotal generalization. Anecdotes are, by definition, not statistical in nature. As such, inductive reasoning that relies on anecdotal generalization will always be weaker than reasoning that uses statistics.

  However, in many situations, anecdotal generalizations are accurate enough to help us solve problems. For instance, if you notice that your flowers wilt in a certain room but liven up in another, you don’t need to conduct a statistically sound experiment to prove your hypothesis.

  But if your boss asks you to come up with a strategy, you’d better have more than anecdotal generalization to go on.

• Prediction. Inductive reasoning can be used to project future estimates based on data you’ve accumulated. Meteorologists rely on highly sophisticated instruments and algorithms that perform inductive reasoning better than humans can (in real time, anyway).

  However, we often use inductive reasoning to predict less complex events. For example, if you always hear your neighbor arrive home between 5-5:15 pm, except on Wednesdays, when he arrives later, you can predict that he won’t be home by 5:15 pm on a Wednesday.

• Causal inference. Inductive reasoning can help you notice patterns of events or phenomena that often occur together. For example, if your car doesn’t start on really cold days, you can assume that the cold is the factor that’s causing your car problems.

  Causal inference can also be statistical, as opposed to the anecdotal version in the given example. Note that causal inference only reasons that a relationship exists — it doesn’t help you determine why the relationship exists.

• Argument from analogy. This is similar to inductive generalization, but is differentiated by relying only on relevant factors at play. For instance, if you observed that a black mesh screen worked really well for keeping bugs out of your patio area, you might reason that a similar material might work well to keep fireflies in a jar while letting in air.

  You wouldn’t, however, reason that that any black material would work as well, because the material is the relevant factor, not the color.

The Benefits and Limitations of Inductive Reasoning

There are many good times to use inductive reasoning in your personal and professional lives.

Benefits

• Generates solutions. Inductive reasoning helps to come up with a multitude of strategies and tactics for solving an issue.

• Observable results. If you make a decision using inductive reasoning, you can show your initial claim’s validity after witnessing the results.

• Quick decisions. Inductive reasoning allows us to make quick and accurate decisions based on our experiences, so we don’t have to stop and think through every choice.

Limitations

• Limited information. The ironic part is that what makes inductive reasoning so useful is also part of its weakness. Any hypothesis you come up with requires confirmation to be validated as true. Of course, the more data you have initially, the more likely your inital hypothesis is true.

• Biased. It’s easy to become biased using inductive reasoning. People get stuck into one way of doing things because it’s always worked for them, not realizing that there’s a better way.

• Personal. For more anecdotal inductive reasoning, you’re limited to your experiences, which all of have one thing in common: you. For example, if every dog you meet is friendly to you, you might wrongly assume that all dogs are friendly to everyone.

How to Improve Your Inductive Reasoning

While inductive reasoning is fairly intuitive, there are certain skills that can help you sharpen your logic and reasoning, especially for certain workplace situations. Here are a few skills you can focus on to build and improve your inductive reasoning.

1. Improve your critical thinking skills. After all, inductive reasoning is just a logical process and being able to think critically and be analytic about the ideas and facts at hand will help you come to better conclusions.

   Don’t be afraid to approach an issue in multiple ways and think about it differently. This can ultimately help you figure out the best solution.

2. Become more detail-oriented. In order to inductively come to a good generalization or conclusion, you need to notice the details and the specifics of a situation. Pay attention to the small things and see how you can build larger inferences from everything around you.

3. Practice your pattern recognition. One type of inductive reasoning is predictive induction, or using the past to predict something. Being able to notice patterns can then help you come to a logical conclusion.

   For example, say you notice how sales have been slow at the beginning of each month but spike in the last week of the month, you can assume this month will follow this pattern and create incentives for consumers to buy your product before the last week of the month.

4. Sharpen your memory. Similar to being able to recognize patterns, you’ll need to remember events and details in order to see all the details that contribute to an inductive conclusion. This doesn’t mean all the details and patterns need to live in your head.

   You can always keep notes to remind you of events and figures, just remember to take those notes and reference them when you’re trying to make an inductive generalization.

5. Strengthen your emotional intelligence. Decisions and reasoning are not entirely fact-based. Oftentimes decisions need to consider people and their emotions, which is where emotional intelligence comes into play.

   Emotional intelligence, or EQ, is the ability to observe and understand your own emotions and the emotions of the people around you. Being able to take into account the more human aspect of reasoning and decision-making will make you a stronger decision maker.

Though inductive reasoning is not strictly a professional skill, being a strong reasoner can help you tremendously in your work.

Think of inductive reasoning as a life skill. It’s broadly applicable in both your personal and professional lives, but it’s a necessary tool to have for navigating life in general.

Now that you know what inductive reasoning is, you can be more aware of when you’re using it and sharpen your logical reasoning skills.

How to Showcase Your Inductive Reasoning Skills

Hiring managers and recruiters love to hire employees who can think through problems logically and arrive at sound decisions. Let’s look at a few ways to highlight your inductive reasoning skills during a job search:

• On your resume. “Inductive reasoning” would look pretty awkward in your resume’s skills section, but that doesn’t mean you can’t exhibit the spirit of the term. Think of accomplishments where you leveraged your ability to reason out a general strategy based on specific details. Then, incorporate those into your work experience section.

  Most importantly, use language from the job description. For example, if “generate solutions for XYZ” is part of the job responsibilites, change your resume’s bullet points from “Solved XYZ” to “generated solutions to XYZ.” This will help your resume pass through applicant tracking systems, and the hiring manager will also appreciate it.

• In your cover letter. Much like your resume, your cover letter should detail your most impressive accomplishments. To incorporate inductive reasoning, write about a time when you solved a problem or generated a strategy based on your observation of particular details.

• During an interview. You’ll often be asked about your decision-making process during a job interview. Think about times when you employed inductive reasoning to your advantage, but also incorporate any other strategies you use to solve problems.

  Most of all, structure your answers using the STAR method. This is a great strategy for answering behavioral interview questions about your past performance. STAR stands for Situation, Task, Action, Result, and it works well for telling concise, impactful stories that have a coherent narrative.

Inductive vs. Deductive Reasoning

On the flip side of inductive reasoning is deductive reasoning. While inductive reasoning goes from the specific to the general, deductive reasoning goes from the general to the specific. Think of them as two sides of the same coin.

Both are useful reasoning tactics, but they apply to different situations. A common understanding of deductive reasoning, also called a syllogism, includes two premises, a big one and a small one, then a logical conclusion. Using this logic, a big premise could be “A is the same as B” and a small premise could be “C is the same as A”, and you conclude that C is also B.

Let’s look at a real-world example of deductive reasoning. Say you need to drive to the store to buy groceries.

You know that it’s dangerous to drive in the snow and you also see that it’s currently snowing. From this, you deduce that it’s dangerous to drive right now and you might decide to wait until tomorrow to go grocery shopping. We use deductive reasoning every day to make decisions like these, including in professional settings.

We’re so used to using both inductive and deductive reasoning that we usually don’t have to stop and think about which reasoning to use. Sometimes, however, you might want to stop and consider which reasoning will help you get to a better solution or conclusion.

Think of it this way — if you want to go from something specific, like a monthly sales figure, to something general, like future sales strategies, you should think inductively. On the other hand, if you want to go from something general, like a theory, to a specific fact, deductive reasoning can help you get there.

Inductive vs. Abductive Reasoning

A third type of reasoning that is often confused with inductive or deductive reasoning is abductive reasoning.

Abductive reasoning creates less reliable conclusions than the other two types because it takes the limited amount of information you have and creates an explanation for them that may or may not be true.

While you shouldn’t fully rely on this reasoning to make decisions, it can be helpful for coming up with and testing solutions to problems you’re trying to solve.

You may see detectives on TV shows use this type of reasoning to find and catch criminals and doctors in real life use it to diagnose patients.

Here is an example of abductive reasoning:

You come home to find your dog home alone with muddy paws. You also see dirty dishes in the sink that weren’t there when you left and decide that the best explanation is that your husband came home during his lunch break and let the dog out.

Author

Amanda Covaleski

Amanda is a writer with experience in various industries, including travel, real estate, and career advice. After taking on internships and entry-level jobs, she is familiar with the job search process and landing that crucial first job. Included in her experience is work at an employer/intern matching startup where she marketed an intern database to employers and supported college interns looking for work experience.