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The fallacy of suppressed correlative is a type of argument that tries to redefine a correlative (one of two mutually exclusive options) so that one alternative encompasses the other, i.e. making one alternative impossible. [1] This has also been known as the fallacy of lost contrast [2] and the fallacy of the suppressed relative. [3]
A correlative conjunction is a relationship between two statements where one must be false and the other true. In formal logic this is known as the exclusive or relationship; traditionally, terms between which this relationship exists have been called contradictories .
Correlative-based fallacies. Suppressed correlative – a correlative is redefined so that one alternative is made impossible (e.g., "I'm not fat because I'm thinner than John."). [18] Definist fallacy – defining a term used in an argument in a biased manner (e.g., using "loaded terms"). The person making the argument expects that the ...
[1] [2] The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc ('with this, therefore because of
(28). According to Formalist critics, this action of creating an emotion through external factors and evidence linked together and thus forming an objective correlative should produce an author's detachment from the depicted character and unite the emotion of the literary work. The "occasion" of Eugenio Montale is a further form of correlative ...
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The continuum fallacy (also known as the fallacy of the beard, [44] [45] line-drawing fallacy, or decision-point fallacy [46]) is an informal fallacy related to the sorites paradox. Both fallacies cause one to erroneously reject a vague claim simply because it is not as precise as one would like it to be.
Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to reality.