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When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy. The consequences of such misinterpretations can be quite severe. For example, in medical science, correcting a falsehood may take decades and cost lives.
McNamara fallacy (quantitative fallacy) – making an argument using only quantitative observations (measurements, statistical or numerical values) and discounting subjective information that focuses on quality (traits, features, or relationships).
An example of the base rate fallacy is the false positive paradox (also known as accuracy paradox).This paradox describes situations where there are more false positive test results than true positives (this means the classifier has a low precision).
[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
In statistics, it may involve basing broad conclusions regarding a statistical survey from a small sample group that fails to sufficiently represent an entire population. [1] [6] [7] Its opposite fallacy is called slothful induction, which consists of denying a reasonable conclusion of an inductive argument (e.g. "it was just a coincidence").
G. I. Joe fallacy, the tendency to think that knowing about cognitive bias is enough to overcome it. [65] Gambler's fallacy, the tendency to think that future probabilities are altered by past events, when in reality they are unchanged. The fallacy arises from an erroneous conceptualization of the law of large numbers. For example, "I've ...
In the above example, if 99% of people are taller than 26 inches, then the probability of the conclusion being true is 99%. Two dicto simpliciter fallacies can occur in statistical syllogisms. They are "accident" and "converse accident". Faulty generalization fallacies can also
Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions.