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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.
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.
For example, by truncating the bottom of a line or bar chart so that differences seem larger than they are. Or, by representing one-dimensional quantities on a pictogram by two- or three-dimensional objects to compare their sizes so that the reader forgets that the images do not scale the same way the quantities do.
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 ]
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
The Shorenstein Center at Harvard University defines disinformation research as an academic field that studies "the spread and impacts of misinformation, disinformation, and media manipulation," including "how it spreads through online and offline channels, and why people are susceptible to believing bad information, and successful strategies for mitigating its impact". [23]
When enough hypotheses are tested, it is virtually certain that some will be reported to be statistically significant (even though this is misleading), since almost every data set with any degree of randomness is likely to contain (for example) some spurious correlations. If they are not cautious, researchers using data mining techniques can be ...