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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.
They also note the very large amount of false information that regularly spreads around the world, overwhelming the hundreds of fact-checking groups; caution that a fact-checker systemically addressing propaganda potentially compromises their objectivity; and argue that even descriptive statements are subjective, leading to conflicting points ...
Spreading false information can also seriously impede the effective and efficient use of the information available on social media. [126] An emerging trend in the online information environment is "a shift away from public discourse to private, more ephemeral, messaging ", which is a challenge to counter misinformation.
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.
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
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.
According to Derakhshan, examples of malinformation can include "revenge porn, where the change of context from private to public is the sign of malicious intent", or providing false information about where and when a photograph was taken in order to mislead the viewer [3] (the picture is real, but the meta-information and its context is changed).
Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".