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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, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. Since the sample does not include all members of the population, statistics of the sample (often known as estimators ), such as means and quartiles, generally differ from the statistics of ...
Misuse of statistics – Use of statistical arguments to assert falsehoods; Overfitting – Flaw in mathematical modelling; Pareidolia – Perception of meaningful patterns or images in random or vague stimuli; Post hoc analysis – Statistical analyses that were not specified before the data were seen
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.
Examples of observer bias extend back to the early 1900's. One of the first recorded events of apparent observer bias was seen in 1904, with the case of "Clever Hans". Clever Hans was a horse whose owner, Wilhem von Olson, claimed could solve arithmetic equations. Von Olson would ask Clever Hans a series of questions involving arithmetic ...
This method can be extended to determining the validity of a sampling frame by taking a sample directly from the target population and then taking another sample from the data frame in order to estimate under-coverage. [9]
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A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors ...