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In [[statistics]], the term "error" arises in two ways. Firstly, it arises in the context of [[decision making]], where the '''probability of error''' may be considered as being the probability of making a wrong decision and which would have a different value for each type of error. Secondly, it arises in the context of [[statistical modelling]] (for example regression) where the model's predicted value may be in error regarding the observed outcome and where the term '''probability of error''' may refer to the probabilities of various amounts of error occurring.
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The '''probability of error''' is similarly distinguished.
* For a Type I error, it is shown as α (alpha) and is known as the ''size'' of the test and is 1 minus the [[Specificity (tests)|specificity]] of the test. This quantity is sometimes referred to as the confidence of the test, or the level of significance (LOS) of the test.
* For a Type II error, it is shown as β (beta) and is 1 minus the [[Statistical power|power]] or 1 minus the [[sensitivity (tests)|sensitivity]] of the test.
==Statistical and econometric modelling==
The fitting of many [[statistical model|models]] in statistics and [[econometrics]] usually seeks to minimise the difference between observed and predicted or theoretical values. This difference is known as an ''error'', though when observed it would be better described as a ''[[Errors and residuals in statistics|residual]]''.
The error is taken to be a [[random variable]] and as such has a [[probability distribution]]. Thus distribution can be used to calculate the probabilities of errors with values within any given range.
== References ==
{{Reflist}}
* https://www.bartleby.com/subject/engineering/electrical-engineering/concepts/probability-of-error
{{DEFAULTSORT:Probability Of Error}}
[[Category:Errors and residuals]]
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