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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,and too misleading.

Examples include:

• "This Web site looks OK to me on my computer; therefore, it will look OK on your computer, too": Fallacious because many computers present content differently.
• "Macrohard 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.

[edit] See also

• Faulty generalization
• Accident (fallacy)
• Converse accident
• Cognitive distortion

[edit] External links and references

• Fallacy: Hasty Generalization, Michael C. Labossiere's Fallacy Tutorial Proes:Generalización apresurada

he:הכללה חפוזה nl:Overhaaste generalisatie

This page uses content from the English-language version of Wikipedia. The original article was at Hasty generalization. The list of authors can be seen in the page history. As with Psychology Wiki, the text of Wikipedia is available under the GNU Free Documentation License.