Inductive reasoning involves using patterns from small datasets to come up with broader generalizations. For example, it is used in opinion polling when you poll 1,000 people and use that data to come up with an estimate of broader public opinion.
Typically, inductive reasoning moves from the specific to the general; and can be understood as educated guesses, assumptions and/or hypotheses drawn from specific incidents.
However, it also has its weaknesses. It cannot provide concrete evidence because it always relies extrapolation and probability.
Inductive logic or inductive reasoning is often contrasted with deductive reasoning which is where the general moves to the specific (in other words: what is generally assumed to be true as a broader phenomenon is assumed to hold in a specific case or circ*mstance).
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Advantages and Disadvantages of Inductive Reasoning
| Pros of Inductive Reasoning | Cons of Inductive Reasoning |
|---|---|
| PRO: When you have a big enough sample set, inductive reasoning can be highly accurate in developing general ideas. | CON: Inductive reasoning can lead to incorrect conclusions, especially when a dataset is too small to be an accurate representation of the whole. |
| PRO: Inductive reasoning enables us to model big phenomena that are impossible to directly measure, such as how many stars there are in the universe. | CON: The more generalized our assumptions become, the less likely they are to be accurate. |
| PRO: Inductive reasoning is used frequently in public policy settings to create targeted interventions for at-risk populations (this is also true of deductive reasoning). | CON: Inductive reasoning leads to stereotyping and untrue assumptions about populations that have not been directly examined as case studies (this is also true of deductive reasoning). |
Well-Formulated Inductive Reasoning Examples
1. Polling and Surveys
“We surveyed 1,000 people across the county and 520 of them said they will vote to re-elect the mayor. We estimate that 52% of the county will vote for the mayor and he will be re-elected.”
Many statisticians make a living from conducting tried-and-true inductive reasoning studies. We often call this “polling data”. Polls will look at a sample size that is often large enough to have a 95% probability of being correct (that is p = <0.05) which is the generally accepted threshold of probability in academic studies.
Polls can help governments and politicians to create policies that are responsive to popular opinion.
However, polls are not always right, and often, statisticians have to re-calibrate their metrics after every general election to get a better understanding of polling bias.
For example, if the statisticians conduct their polls by phone, it may be the case that older people tend to answer their phone more than younger people, and older people may skew their vote in one way or another, which skews the overall polling numbers! They need to account for these biases, which makes their job of making generalizations from patterns very difficult at times.
2. Bonus Structure
“In a study of fifteen employees in my business, I found that a 10% bonus structure raised revenues by 20%. I will now roll-out the bonus structure to all employees.”
In this example of reasoning, a business owner has used a small dataset to identify a trend, which gave them sufficient confidence to roll out their intervention across the entire workplace.
If the business owner didn’t do this initial study, they wouldn’t have any indicative data to rely upon in order to feel confident about their decision. Here, we see how inductive reasoning can be used to help us make more informed decisions.
This doesn’t mean that the business owner will have the same success rate when he introduces the bonuses to everyone, but at least he can proceed with greater confidence than before.
3. Seasonal Trends
“For five years in a row, I have seen bears in the woods in June but not May. This year, I expect to wait until June to see a bear in the woods.”
We can also use inductive reasoning to make assumptions in our own lives. In the above example, a person who lives near the woods has identified a seasonal trend that allows them to generalize and predict future patterns.
This sort of seasonal prediction has been around for millennia. Nomads saw patterns in the land and decided to go on annual migrations based on their hypotheses that certain lands would be more fertile at certain times of year. Similarly, agriculturalists use seasonal trends to reason about when to plant their seeds. This doesn’t mean every year will be perfect (to this day, some seasons are terrible for crop yield).
4. Archaeological Digs
“We dug up three pots within a thirty square foot area. We should focus our dig efforts on this area to see what else we can dig up.”
Archaeology also regularly relies upon inductive reasoning. An archaeologist will find signs of human occupation in a location and use those signs as reason the intensify focus on that area.
In these instances, they are inducing that there are likely to be more remnants of civilization around the first remnants due to the assumption that humans may have settled or camped in that specific location.
5. Traffic Patterns
“I have noticed that traffic is bad between 7.30am and 9am. I will drive to the grocery store after 9am to avoid the traffic.”
We even use inductive reason regularly when planning out our days. We make observations about the things around us and use them to make generalizations and predictions.
In the above example, the person has noticed that traffic is worst just before the work day begins, so avoids driving during that period. This is a generalization that can help the person make informed decisions. While it’s not guaranteed that traffic will be better at 9.30am than 8.30am (there may be a car crash at any time of day!), inductive reasoning states that it is likely that traffic will be better at 9.30am than 8.30am.
Poorly-Formulated Inductive Reasoning Examples
6. Dog Breeds
“Despite what the government says about Pitt Bulls, the only Pitt Bulls I have ever met were extremely friendly and sweet. Pitt Bulls must therefore not be a dangerous breed.”
While it may well be the case that this person has not personally encountered a hostile or aggressive Pitt Bull, numerous studies have been done indicating that Pitt Bulls, on average, are more aggressive than other dog breeds; whether or not this is inherently true remains speculation. Many cities have also banned the breed since they’ve resulted in the vast majority of dog fatally-related incidents and injuries, relative to the other dog breeds that exist.
This example illustrates how inductive logic goes from specific incidences and applies them as a general rule or conclusion on a given matter.
7. Job Salary and Occupation
“John is a lawyer, and he makes a lot of money. All lawyers make tons of money.”
Appearances can be deceiving, and though basic logic might indicate that something is true, it does not always hold in each situation. While it’s reasonable to assume that people within a certain occupation may earn a lot of money since, generally speaking, the job is associated with a higher salary—it is not always the case in every circ*mstance.
Some lawyers, for example, do pro-bono work, others may be employed by the government and work as public defenders for individuals that may lack the means to hire their own legal counsel.
8. Nationality
“My dad is Russian and he has blonde hair and blue eyes. All Russian people must have blonde hair and blue eyes.”
This illustrates the inductive reasoning fallacy by moving from an isolated or single case and applying it as a general rule or broadly applicable conclusion. We know that just because a person bears certain physical traits that may be generally affiliated with a geographical region, that does not mean all individuals from the same place will share those same physical traits.
This shows how inductive reasoning can result in incorrect conclusions and/or false assumptions by using specific instances to draw conclusions.
9. Left-Handedness
“All of my siblings are left-handed, and we are all talented artists. People that are left-handed are more creative and artistically inclined than those that are right-handed.”
It could seem reasonable for this person to assume (based on the evidence that they are exposed to,) that left-handed people are naturally more creative and artistic than their right-handed counterparts. Despite appearances, it is not proven that left-handed people are in fact more artistic than right-handed people.
The misstep in logic occurs from making the move from the specific to the general without having sufficient evidence to substantiate the claim as a generally applicable rule.
10. Rainy Weather
“I was in Seattle for a week, and it rained for all seven days I was there. It is always raining in Seattle.”
There’s no question that Seattle gets a lot of rain and is objectively regarded as a very rainy city. Even still, it would be false to conclude that it rains every single day without fail since this is not the case.
To correct the false conclusion or error in logic, we would revise the statement to some form of the following—each day I was in Seattle it rained; therefore, it is often raining in Seattle.
11. Buying Avocados
“While shopping for groceries, I was in the produce section checking for ripe avocados. I picked up one avocado and it was not ripe enough to eat. I picked up another and it was also underripe. There must not be any ripe avocados at this grocery store.”
While it’s possible that there are not any ripe avocados at the grocery store the person is perusing, this is not conclusive until he or she has inspected each avocado in the bin on how its ripeness. It’s clear that picking up a few avocados and determining that they are not ripe enough to eat does not necessarily indicate the remaining avocados in the bin will be underripe. This abrogates logic and demonstrates the error in inductive reasoning.
12. Food Poisoning
“The last time I ate at this Japanese restaurant I got terrible food poisoning. Do not go and eat at this Japanese restaurant because you will get food poisoning and be extremely sick.”
One incident of food poisoning does not indicate a general pattern or broad truth, and it certainly does not follow that just because a person got food poisoning from eating at a restaurant one time, anyone who eats at that same restaurant will necessarily get food poisoning.
The problem with fallacies in inductive reasoning is that it looks to establish a claim on what is true and factual in general, and while it may well be true in an individual case, it is unlikely to hold in each case without fail.
13. Buying A Mattress
“I have purchased four different mattresses on Amazon. None of them were comfortable, and so I returned all four. Amazon doesn’t have good-quality mattresses.”
This takes a similar structure to the previous example on buying avocados. It’s clear how it would be tempting for this person to conclude, based on their personal experience, that Amazon doesn’t have decent mattresses available to purchase.
However, until the person has actually tried each mattress for sale on Amazon, they cannot say conclusively that all mattresses for sale on Amazon are of poor quality. This would be a false assumption that uses the fallacy of inductive reasoning to draw a conclusion.
14. Penguins
“Penguins are birds and they can’t fly. Therefore, it must be true that birds cannot fly.”
Penguins are a kind of bird and cannot fly; but this does not mean that birds, in general, cannot fly. We know birds can fly—so to assume that birds cannot fly because penguins cannot fly is false and uses flawed inductive logic to formulate its conclusion.
If a person saw a crow and said “crows are birds and can fly, so all birds can fly”, it would also be a false inductive generalization. The person should gather a larger dataset of different types of birds before formulating their hypothesis.
15. Rap Music
“The few rap songs that I’ve listened to included remarks that were inappropriate. Therefore, all rap music is inappropriate.”
While rap music can certainly have some uncouth lyrics, it is surely not the case that rap music is inherently bad, or that every single rap song that exists is not acceptable. There are many rap musicians who rap positive lyrics.
Therefore, this is an overgeneralization (often used by parents!) that aims to exclude the good with the bad, rather than taking a more nuanced look at the issue at hand.
Read Next: Abductive Reasoning Examples
Conclusion
Inductive reasoning is a useful tool in education (see: inductive learning), scholarly research and everyday life in order to identify trends and make predictions. It is a type of inference that helps us to narrow-down the field of likely consequences of actions and empowers us to make more effective decisions.
However, it’s also important to remember that the fallacy of inductive reasoning is incredibly common and can crop up in regular conversation, debates, the media and online discussions. It’s easy to jump to false conclusions or to assume a general pattern where one may not exist.
Generally, we can resolve the problem of hasty generalizations by ensuring our initial dataset is truly representative and large enough that induction can occur with a smaller margin of error.
Chris Drew (PhD)
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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]
