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The misuse of Statistics can trick the observer who does not understand them into believing something other than what the data shows or what is really 'true'. That is, a misuse of statistics occurs when an argument uses statistics to assert a falsehood. In some cases, the misuse may be accidental.
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. [27]
Printable version; In other projects ... Pages in category "Probability fallacies" The following 7 pages are in this category, out of 7 total. ... Statistics; Cookie ...
A set of 100 randomly generated points displayed on a scatter graph. Examining the points, it is easy to identify apparent patterns. In particular, rather than spreading out evenly, it is not uncommon for random data points to form clusters, giving the (false) impression of "hot spots" created by some underlying cause.
False precision (also called overprecision, fake precision, misplaced precision, and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
Print/export Download as PDF; Printable version; ... Pages in category "Statistical paradoxes" The following 18 pages are in this category, out of 18 total.
The usage of percentages as labels on a pie chart can be misleading when the sample size is small. [8] Making a pie chart 3D or adding a slant will make interpretation difficult due to distorted effect of perspective. [9] Bar-charted pie graphs in which the height of the slices is varied may confuse the reader. [9]
The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]