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Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
The use of fallacies is common when the speaker's goal of achieving common agreement is more important to them than utilizing sound reasoning. When fallacies are used, the premise should be recognized as not well-grounded, the conclusion as unproven (but not necessarily false), and the argument as unsound.
The four common statistical ecological fallacies are: confusion between ecological correlations and individual correlations, confusion between group average and total average, Simpson's paradox, and confusion between higher average and higher likelihood. From a statistical point of view, these ideas can be unified by specifying proper ...
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 ...
Pages in category "Probability fallacies" The following 7 pages are in this category, out of 7 total. ... Statistics; Cookie statement; Mobile view ...
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).
A special subclass of the informal fallacies is the set of faulty generalizations, also known as inductive fallacies. Here, the most important issue concerns inductive strength or methodology (for example, statistical inference ).
The four common statistical ecological fallacies are: confusion between ecological correlations and individual correlations, confusion between group average and total average, Simpson's paradox, and other statistical methods.