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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.〔(【引用サイトリンク】 accessdate=2008-10-01 )〕 Its opposite fallacy is called slothful induction, or denying a reasonable conclusion of an inductive argument (e.g. "it was just a coincidence"). ==Examples== Hasty generalization usually shows this pattern #X is true for A. #X is true for B. #X is true for C. #X is true for D. #Therefore, X is true for E, F, G, etc. For example, if a person travels through a town for the first time and sees 10 people, all of them children, he 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Hasty generalization」の詳細全文を読む スポンサード リンク
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