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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.
Misleading graphs are often used in false advertising. One of the first authors to write about misleading graphs was Darrell Huff, publisher of the 1954 book How to Lie with Statistics. The field of data visualization describes ways to present information that avoids creating misleading graphs.
Simpson's paradox has been used to illustrate the kind of misleading pancakes 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 President's Falsehoods, Misleading Claims and Flat-Out Lies. [141] [142] By October 9, 2019, The Washington Post ' s fact-checking team documented that Trump had "made 13,435 false or misleading claims over 993 days". [143] On October 18, 2019, the Washington Post Fact Checker newsletter described the situation: A thousand days of Trump.
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 new ad features giant on-screen text with the words “global war,” attributing them to a July article by the media outlet Axios, as the ad’s narrator says, “Their weakness invited wars.”
Fake news is false or misleading information presented as news. [10] [16] The term as it developed in 2017 is a neologism (a new or re-purposed expression that is entering the language, driven by culture or technology changes). [17]
George Box. The phrase "all models are wrong" was first attributed to George Box in a 1976 paper published in the Journal of the American Statistical Association.In the paper, Box uses the phrase to refer to the limitations of models, arguing that while no model is ever completely accurate, simpler models can still provide valuable insights if applied judiciously. [1]