Which Option Is The Clearest Example Of Inductive Reasoning

Inductive reasoning, or inductive logic, is one of the three types of reasoning we use in everyday life. This type of reasoning is often called “bottom-up” reasoning, as it involves taking individual instances and inferring a generalized conclusion from them. If that sounds confusing, don’t worry — it’s something you already do on a regular basis.

If you want to know what inductive reasoning is and what it’s useful for, then you’ve come to the right place. This article will also cover how to use, strengthen, and showcase your inductive reasoning skills.

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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 often uses 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, according to Merriam-Webster, an “inference of a generalized conclusion from particular instances.” All that means is that you take some observations, such as: “every leaf I’ve seen is green” and expand it to: “therefore all leaves must be green.”

As shown here, inductive reasoning isn’t always correct. It’d be more correct to say “therefore most leaves must be green.” The concept can be a little hard to grasp with just one example, so we’ll expand it a bit.

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 at all 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.

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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 reasoning isn’t monolithic. In can be used in a variety of circumstances, but depending on the information you’re starting with and your induction, you’re using slightly different types of reasoning. Here are the different kinds 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 generalization 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 and 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 any black material would work as well because the material is the relevant factor, not the color.

The benefits and limitations of inductive reasoning

Inductive reasoning can be tremendously useful in certain circumstances, but like all things, there are also limitations. If you’re unsure of the best times to make use of inductive reasoning, seeing its benefits and limitations may help you with deciding when to rely on it.

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 initial hypothesis is true.

• Biased. It’s easy to become biased using inductive reasoning. People get stuck in 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

Induction is something most people do on a regular basis, so it may seem odd to work to strengthen the skill. However, there are things you can do to not only get better at inductive reasoning but to make your predictions more accurate. Here are a few ways that you can work to 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.

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   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.

How to showcase your inductive reasoning skills

Finding a candidate who can reason things through is always a boon for hiring managers. But you can’t just say that you’re an expert at inductive reasoning, or put it directly on your resume. So how do you highlight your reasoning skills? Here are a few ways to say you have strong inductive reasoning skills without actually saying that.

• 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 responsibilities, 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 reasoning FAQ

1. What’s the difference between inductive reasoning and deductive reasoning?

   The difference between inductive reasoning and deductive reasoning is that induction goes from specific to general, while deduction goes from general to specific. For example, inductive reasoning would tell you that the last time you drove in the snow, your car skidded off the road, so you’d infer that every car skids in the snow.

   Deductive reasoning would tell you that driving in the snow is dangerous, so therefore it’s dangerous for you to drive in the snow. So you may postpone your trip to the store during a snowstorm for that reason.

2. What’s the difference between inductive reasoning and abductive reasoning?

   The difference between inductive reasoning and abductive reasoning is that inductive reasoning works from observations to generalizations, while abductive reasoning takes a set of clues you have and generates the best explanation for them. The latter is most often seen used by detectives in TV shows and doctors trying to diagnose patients.

   An example of abductive reasoning is: 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 spouse came home during his lunch break and let the dog out.

3. How is inductive reasoning used in research?

   Inductive reasoning is used in two different ways in research: by helping to form hypotheses and in inductive research. Inductive reasoning is used to form hypotheses by using observations to form a testable hypothesis. These hypotheses may be wrong, as they work from a small amount of data, but the point of the study is to confirm or disprove the theory.

   Inductive research is based on searching for patterns. It starts off by gathering data or observations, and then combing through it to search for patterns. From this, you’re able to make general conclusions.

References

1. Merriam-Webster — ‘Deductive’ vs. ‘Inductive’ vs ‘Abductive’ Reasoning

2. MasterClass — What Is Inductive Reasoning?

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