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Hasty Generalization Fallacy Examples. 1. Junk food. Jane loves fast food—it’s all she eats. She’s not concerned about her eating habits because she has a friend that, “only eats fast food and hasn’t had any health problems for months.”
A Hasty Generalization Fallacy occurs when someone makes a broad statement based on a very small or unrepresentative sample of data. Stick around, and you'll not only get a clear explanation of this concept but also walk away with numerous real-world examples.
Hasty generalization fallacy example “I’ve met two people in Greece so far, and they were both nice to me. So, all the people. I will meet in Greece will be nice to me.” Here, the speaker makes an absolute statement. In other words, they imply zero error margin (“all the people”).
The hasty generalization fallacy, also known as the overgeneralization fallacy, is the logical fallacy of making a claim based on a sample size far too small to support the claim. Whether a sample size is large enough to support a claim depends on the specific claim.
A hasty generalization is a fallacy in which a conclusion that is reached is not logically justified by sufficient or unbiased evidence. This type of claim can also be referred to as an insufficient sample; a converse accident; a faulty generalization; a biased generalization; jumping to a conclusion; secundum quid; and neglect of qualifications.
Among notable examples in recent years was former U.S. President Donald Trump's generalizations about Mexicans, while he was still a presidential candidate.
This fallacy occurs when an argument is based on a body of evidence that is simply too small. For instance, if your uncle was a lifelong cigarette smoker who lived into his nineties, it would be a hasty generalization to claim that smokers have a high life expectancy based on such limited evidence.