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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.
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, [ 1 ] [ 2 ] [ 3 ] and is particularly problematic when frequency data are unduly given ...
In statistics, a misleading graph, ... For example, log scales may give a height of 1 for a value of 10 in the data and a height of 6 for a value of 1,000,000 ...
In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample [1] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. [2]
For example, if a pharmaceutical company wishes to explore the effect of a medication on the common cold but the data sample only includes men, any conclusions made from that data will be biased towards how the medication affects men rather than people in general. That means the information would be incomplete and not useful for deciding if the ...
While true, that statistic also ignores the context that UK’s 45 wins in that span include 15 against Group of Five or FCS foes. UK is sixth in the league in conference wins since 2018.
Gould explains how the statistic that peritoneal mesothelioma, the form of cancer with which he was diagnosed in 1982, has a "median survival time of eight months" is misleading. [22] "Lies, Damn Lies and Statistics" is the title of episode 21 in the first season of NBC drama The West Wing. [23]
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.