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Hasty generalization is the fallacy of examining just one or very few examples or studying a single case and generalizing that to be representative of the whole class of objects or phenomena. The opposite, slothful induction , is the fallacy of denying the logical conclusion of an inductive argument, dismissing an effect as "just a coincidence ...
Persuasive definition – purporting to use the "true" or "commonly accepted" meaning of a term while, in reality, using an uncommon or altered definition. (cf. the if-by-whiskey fallacy) Ecological fallacy – inferring about the nature of an entity based solely upon aggregate statistics collected for the group to which that entity belongs.
Generalizing quickly and sloppily (hasty generalization) (secundum quid) Using an argument's connections to other concepts or people to support or refute it, also called "guilt by association" (association fallacy) Claiming that a lack of proof counts as proof (appeal to ignorance) In humor, errors of reasoning are used for comical purposes.
[16]: 147 The generalization, in this case, ignores that insanity is an exceptional case to which the general rights of property do not unrestrictedly apply. Hasty generalization, on the other hand, involves the converse mistake of drawing a universal conclusion based on a small number of instances.
Inductive reasoning is any of various methods of reasoning in which broad generalizations or principles are derived from a body of observations. [1] [2] This article is concerned with the inductive reasoning other than deductive reasoning (such as mathematical induction), where the conclusion of a deductive argument is certain, given the premises are correct; in contrast, the truth of the ...
In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample [ 1 ] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected ...
The most common form of the fallacy is the use of anecdotes to create a fallacy of Hasty Generalization. Language surrounding the fallacy must indicate a logical conclusion, and includes absolute statements such as "every", "all", and so forth. However, other forms of the fallacy exist.
The term is not commonly encountered in statistics texts and there is no single authoritative definition. It is a generalization of lying with statistics which was richly described by examples from statisticians 60 years ago. The definition confronts some problems (some are addressed by the source): [2]