I had a conversation the other day with my friend, Hasty:
Me: “Canada has a good public health system: they provide care to 100% of their population.”
Hasty: “The Canadian healthcare system sucks! One of my friends from Canada goes to the United States for every medical procedure he needs; otherwise, he’d have to wait at a public hospital.”
This is an example of a logical fallacy called “Hasty Generalization.”
Hasty generalization happens when someone draws a conclusion based on a sample that is too small to support it. My friend Hasty took a sample of literally one person to counter my claim that Canada, home to 37 million people, has a good healthcare system.
What is Hasty Generalization?
Every generalization starts with an initial sample of things of a certain kind and then makes a claim about all things of that kind, for example:
All the ravens I’ve ever seen are black.
Therefore, all ravens are black.
A generalization is stronger or weaker depending on the size of the initial sample. Hasty generalizations are weak generalizations. A generalization is hasty when we endorse a general claim without having observed a sample large enough to be confident that the claim is true.
Consider an example. The circle below is populated with letters. Here’s a general claim about the letters:
(R) All the letters in the circle are rs.
How confident can you be that this claim is true? Compare two observations:
Observation A shows you only a small sample of the letters. Observation B shows a much larger sample. It should be clear that Observation B provides you with stronger evidence that claim (R) is true. By contrast, Observation A provides you with very weak evidence that claim (R) is true.
If you were to endorse (R) based on an insufficient sample, like Observation A, instead of a larger observed sample, like Observation B, you’d be committing a hasty generalization fallacy.
Hasty generalization is a fallacy because someone is drawing a general conclusion based on a sample size that is too small to support that conclusion. They are jumping to a conclusion “too fast”–that is, without acquiring sufficient evidence to justify the conclusion.
The hasty generalization fallacy is a common fallacy like the straw man fallacy, the slippery slope fallacy, and ad hominem. Fallacies in general have this characteristic: even if their premises are true, they still fall short of telling you whether the conclusion is true or false. In the above example, if you observe only three letters in the circle, you still can’t tell whether (R) is likely true or false.
Other names for hasty generalization include “Fallacy of the lonely fact,” “Statistic of small numbers,” “Faulty generalization,” “Overgeneralization,” “Hasty induction,” and “Unrepresentative sample.”
Many people are tempted to commit the hasty generalization fallacy because they draw general conclusions based on their own experience. The problem is that their experience provides only a sample size of 1, and that sample size is insufficient to support most generalizations.
Here are some more examples of hasty generalization:
“My mom smoked for over 60 years and never had any health issues, so cigarette smoking is not that bad for your health.”
A food company claims on social media, “Men prefer the smell of bacon in the morning because 4 out of 5 men in our office do.”
“I went on a Paleo diet for 3 months and lost 10 pounds. So anyone who goes on a Paleo diet will lose weight.”
Explanation: In each of these three examples, the arguer has insufficient evidence to support a general claim that’s being endorsed. Each arguer takes a sample that is too small to support that more general claim. To support the claim about smokers, a larger sample of cigarette smokers needs to be examined. To support the claim about food companies, a larger population of men needs to be obtained. To support Paleo fan’s claim, more people need to try the diet to have a large enough sample.
How to Counter the Fallacy of Hasty Generalization
Countering the hasty generalization fallacy requires improving your critical thinking skills. Fallacious thinking stems, in this case, from using a sample that is too small to support a general claim. So to counter a hasty generalization you need to point out one thing:
The sample is too small to make an accurate judgment about the entire class.
What Should You Do Instead?
To avoid making hasty generalizations yourself, you need to make sure that you don’t make general claims based on relatively small samples. Instead, withhold judgment about general claims until you’ve made enough observations to support them.
Free thinkers make generalizations that are only as strong as the available evidence, and they’re open to revising their judgments because of that. The strength of your commitment to a generalization should only be as strong as the evidence that supports it. Even if you have a relatively large sample to support a generalization, your commitment to that generalization should still be open to revision as new evidence comes to light.
People make hasty generalizations all the time. Being a free thinker requires you to go against the crowd. It requires you to withhold judgment about claims that others are willing to endorse, and it positions you to be able to explain why their arguments are weak.