Editing Calibration (statistics) (section)

{{short description|Ambiguous term in statistics}}
{{use dmy dates|date=March 2024}}
There are two main uses of the term '''calibration''' in [[statistics]] that denote special types of [[statistical inference]] problems. Calibration can mean
:*a reverse process to [[Linear regression|regression]], where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable;<ref>{{cite book|last1=Cook|first1=Ian|last2=Upton|first2=Graham|date=2006|title=Oxford Dictionary of Statistics|location=Oxford|publisher=Oxford University Press|isbn=978-0-19-954145-4}}</ref>
:*procedures in [[statistical classification]] to determine [[class membership probabilities]] which assess the uncertainty of a given new observation belonging to each of the already established classes.
In addition, calibration is used in statistics with the usual general meaning of [[calibration]]. For example, model calibration can be also used to refer to [[Bayesian inference]] about the value of a model's parameters, given some data set, or more generally to any type of fitting of a [[statistical model]]. As [[Philip Dawid]] puts it, "a forecaster is ''well calibrated'' if, for example, of those events to which he assigns a probability 30 percent, the long-run proportion that actually occurs turns out to be 30 percent."<ref>{{cite journal |doi=10.1080/01621459.1982.10477856 |title=The Well-Calibrated Bayesian |journal=Journal of the American Statistical Association |volume=77 |issue=379 |pages=605–610 |year=1982 |last1=Dawid |first1=A. P}}</ref>