Hasty generalization
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Hasty generalization, 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 or secundum quid, is the logical fallacy of reaching an inductive generalization based on too little 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. Statistics in general can have many problems, especially in surveys where the questions can assume too much, be too vague, or be too misleading.
Examples include:
- "I loved the hit song, therefore I'll love the album it's on". Fallacious because the album might have one good song and lots of filler.
- "This Web site looks OK to me on my computer; therefore, it will look OK on your computer, too". Fallacious because different computers may present content differently.
- "A poll sampled 500 liberal non-religious males living in San Francisco regarding the issue of gay marriage in America. Most of them approved, therefore most Americans everywhere must approve of gay marriage". Fallacious because the sample was not diverse enough to accurately generalize the opinions of all Americans.
- "I got into a fight with a bunch of Asians today. They all knew 10 Animal Huo style Kung Fu. This means that all Asians know 10 Animal Huo style Kung Fu."
- "One divides sixty. Two divides sixty. Three divides sixty. Four divides sixty. Five divides sixty. Six divides sixty. Then, I guess all positive integers divide sixty."
[edit] See also
- Faulty generalization
- Accident (fallacy)
- Loki's Wager
- Converse accident
- Cognitive distortion
- Syllogism
[edit] External links and references
- Fallacy: Hasty Generalization, Michael C. Labossiere's Fallacy Tutorial Pro
- Examples of Unfounded Generalizations @ Humbug! Online.
