Hasty generalization

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Hasty generalization is an informal fallacy of faulty generalization by reaching an inductive generalization based on insufficient evidence—essentially making a hasty conclusion without considering all of the variables. In statistics, it may involve basing broad conclusions regarding the statistics of a survey from a small sample group that fails to sufficiently represent an entire population.[1] Its opposite fallacy is called slothful induction, or denying a reasonable conclusion of an inductive argument (e.g. "it was just a coincidence").

Examples[edit]

Hasty generalization usually shows this pattern

  1. X is true for A.
  2. X is true for B.
  3. Therefore, X is true for C, D, etc.

For example, if a person travels through a town for the first time and sees 10 people, all of them children, they may erroneously conclude that there are no adult residents in the town.

Or: A person is looking at a number line. The number 1 is a square number; 3 is a prime number, 5 is a prime number, and 7 is a prime number; 9 is a square number; 11 is a prime number, and 13 is a prime number. Therefore, the person says, all odd numbers are either prime or square. In reality, 15 is a counterexample.

The 2016 US presidential campaign of Donald Trump was littered with hast generalizations, eg. “When Mexico sends its people, they’re not sending their best. They’re not sending you. They’re not sending you. They’re sending people that have lots of problems and they’re bringing those problems with us. They’re bringing drugs, they’re bringing crime, they’re rapists, and some, I assume, are good people.”[2]

Alternative names[edit]

The fallacy is also known as:

  • Illicit generalization
  • Fallacy of insufficient sample
  • Generalization from the particular
  • Leaping to a conclusion
  • Hasty induction
  • Law of small numbers
  • Unrepresentative sample
  • Secundum quid

When referring to a generalization made from a single example it has been called the fallacy of the lonely fact[3] or the proof by example fallacy.[4]

When evidence is intentionally excluded to bias the result, it is sometimes termed the fallacy of exclusion and is a form of selection bias.[5]

See also[edit]

References[edit]

  1. ^ "Fallacy: Hasty Generalization (Nizkor Project)". Retrieved 2008-10-01. 
  2. ^ "Trump's Hasty Generalization". trueamericanstories.blogspot.com. True American Stories. 4 February 2016. 
  3. ^ Fischer, David Hackett (1970). Historians' Fallacies: Toward a Logic of Historical Thought. HarperCollins. pp. 109–110. ISBN 978-0-06-131545-9. 
  4. ^ Marchant, Jamie. "Logical Fallacies". Retrieved 2011-04-26. 
  5. ^ "Unrepresentative Sample". Retrieved 2008-09-01. 

External links[edit]