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.[1] Its opposite fallacy is called slothful induction, or denying the logical conclusion of an inductive argument (i.e. "it was just a coincidence").
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Examples
Person A travels through Town X for the first time. He sees 10 people, all of them children. Person A returns to his town and reports that there are no adult 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. On the basis of this remark, Person B concludes that:
- Person A's grandfather pawned his watch; or
- Person A's grandfather had expensive tastes in jewelery; or
- Person A's grandfather was ostentatious; or
- Person A's grandfather cannot tell the time any more.
Context is also relevant; in mathematics the Pólya conjecture is true for numbers less than 906,150,257, but fails for this number. Assuming something to be true for all numbers when it has been shown for over 906 million cases would not generally be considered hasty, but in mathematics a statement remains a conjecture until it is shown to be universally true.
Hasty generalization is also the basis for racist beliefs and prejudices - a person will infer an attribute to common to all members of a group based on knowledge of a small sample size of that group. For example, the belief that a given person who is Jewish will be greedy, nit-picky, stingy misers or the belief because a person is black, (s)he will be loud, poor, and criminal.[2][3]
Alternative names
The fallacy is also known as: fallacy of insufficient statistics, fallacy of insufficient sample, fallacy of the lonely fact, generalization from the particular, leaping to a conclusion, hasty induction, law of small numbers, unrepresentative sample, and secundum quid.
When evidence is intentionally excluded to bias the result, it is sometimes termed the fallacy of exclusion and is a form of selection bias.[4]
References
- ^ "Fallacy: Hasty Generalization (Nizkor Project)". http://www.nizkor.org/features/fallacies/hasty-generalization.html. Retrieved 2008-10-01.
- ^ http://www.fff.org/freedom/0400f.asp
- ^ Devine, PG; AJ Elliot (1995). "'Are racial stereotypes really fading?'". Personality and Social Psychology Bulletin 21 (11): 1139–1150. doi:.
- ^ "Unrepresentative Sample". http://www.changingminds.org/disciplines/argument/fallacies/unrepresentative_sample.htm. Retrieved 2008-09-01.
See also
- Accident (fallacy)
- Blind men and an elephant
- Converse accident
- Cognitive distortion
- Hypercorrection
- Loki's Wager
- Statistical significance
- Slothful induction
- Syllogism
- Generalization (logic)
- Generalization error
- Categorical imperative redirected from "Generalization in Ethics"
- Generalizations of Fibonacci numbers
- Generalizations of Pauli matrices
- Derivative (generalizations)
External links
- Fallacy: Hasty Generalization, Michael C. Labossiere's Fallacy Tutorial Pro
- Hasty Generalization, The Fallacy Files
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