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Hasty generalization
From Wikipedia, the free encyclopedia
Hasty generalization is a logical fallacy of faulty generalization by reaching an inductive generalization based on insufficient evidence. It commonly involves basing a broad conclusion upon the statistics of a survey of a small group that fails to sufficiently represent the whole population.
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Person A travels through Town X for the first time. He sees 10 people, all of them white. Person A returns to his town and reports that there are no minority residents in Town X.
Person A and Person B walk past a pawn shop. Person A remarks that a watch in a window display looks like the one his grandfather used to wear.
- Person B concludes that Person A's grandfather pawned his watch
- Person B concludes that Person A's grandfather had expensive tastes in jewelry
- Person B concludes that Person A's grandfather was ostentatious
- Person B concludes that Person A's grandfather can not tell the time any more
The fallacy is also known as: fallacy of insufficient statistics, fallacy of insufficient sample, fallacy of the lonely fact, leaping to a conclusion, hasty induction, law of small numbers, unrepresentative sample and secundum quid.
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This article does not cite any references or sources. (March 2007) Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed. |
- Fallacy: Hasty Generalization, Michael C. Labossiere's Fallacy Tutorial Pro