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Cum hoc ergo propter hoc (Latin for 'with this, therefore because of this'; correlation implies causation; faulty cause/effect, coincidental correlation, correlation without causation) – a faulty assumption that, because there is a correlation between two variables, one caused the other. [57]
An example is a probabilistically valid instance of the formally invalid argument form of denying the antecedent or affirming the consequent. [ 12 ] Thus, "fallacious arguments usually have the deceptive appearance of being good arguments, [ 13 ] because for most fallacious instances of an argument form, a similar but non-fallacious instance ...
Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology ; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one.
The questionable cause—also known as causal fallacy, false cause, or non causa pro causa ("non-cause for cause" in Latin)—is a category of informal fallacies in which the cause or causes is/are incorrectly identified. In other words, it is a fallacy of reaching a conclusion that one thing caused another, simply because they are regularly ...
A faulty generalization is an informal fallacy wherein a conclusion is drawn about all or many instances of a phenomenon on the basis of one or a few instances of that phenomenon. It is similar to a proof by example in mathematics. [1] It is an example of jumping to conclusions. [2]
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The fallacy of the single cause, also known as complex cause, causal oversimplification, [1] causal reductionism, root cause fallacy, and reduction fallacy, [2] is an informal fallacy of questionable cause that occurs when it is assumed that there is a single, simple cause of an outcome when in reality it may have been caused by a number of only jointly sufficient causes.
As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not necessarily imply that the resulting conclusion is false. Statistical methods have been proposed that use correlation as the basis for hypothesis tests for causality, including the Granger causality test and convergent cross mapping .