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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.
In statistics, a misleading graph, also known as a distorted graph, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it. Graphs may be misleading by being excessively complex or poorly constructed.
Pages in category "Misuse of statistics" The following 27 pages are in this category, out of 27 total. This list may not reflect recent changes. ...
The book is a brief, breezy illustrated volume outlining the misuse of statistics and errors in the interpretation of statistics, and how errors create incorrect conclusions. In the 1960s and 1970s, it became a standard textbook introduction to the subject of statistics for many college students.
Data dredging (also known as data snooping or p-hacking) [1] [a] is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives.
Misuse of p-values is common in scientific research and scientific education. p -values are often used or interpreted incorrectly; [ 1 ] the American Statistical Association states that p -values can indicate how incompatible the data are with a specified statistical model. [ 2 ]
Simpson's paradox has been used to illustrate the kind of misleading results that the misuse of statistics can generate. [7] [8] Edward H. Simpson first described this phenomenon in a technical paper in 1951, [9] but the statisticians Karl Pearson (in 1899 [10]) and Udny Yule (in 1903 [11]) had mentioned similar effects earlier.
The phrase is quoted frequently in 1895, but here is a 1894 example: "His less enthusiastic neighbor thinks of the proverbial kinds of falsehoods, “lies, damned lies, and statistics,” and replies: “Reports of large numbers of cases subjected to operation seldom fail to beget a suspicion of unjustifiable risk.”" [10] [11]