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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.
The origin of the phrase "Lies, damned lies, and statistics" is unclear, but Mark Twain attributed it to Benjamin Disraeli [1] "Lies, damned lies, and statistics" is a phrase describing the persuasive power of statistics to bolster weak arguments, "one of the best, and best-known" critiques of applied statistics. [2]
The source of the statistics is very important. The first questions to ask when reading statistical research results is who did the study and why. The source of the statistics is very important.
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.
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 ]
Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics, [74] by Darrell Huff, outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).
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.