In statistics, the terms type I error (also, α error, false alarm rate (FAR) or false positive) and type II error (β error, miss rate or a false negative) are used to describe possible errors made in a statistical decision process. In 1928, Jerzy Neyman (1894-1981) and Egon Pearson (1895-1980), both eminent statisticians, discussed the problems associated with "deciding whether or not a particular sample may be judged as likely to have been randomly drawn from a certain population" (1928/1967, p. 1), and identified "two sources of error", namely:
- Type I (α): reject the null hypothesis when the null hypothesis is true, and
- Type II (β): accept the null hypothesis when the null hypothesis is false
In systems theory an additional type III error is often defined[1]:
- Type III (δ): asking the wrong question and using the wrong null hypothesis
In 1930, they elaborated on these two sources of error, remarking that "in testing hypotheses two considerations must be kept in view, (1) we must be able to reduce the chance of rejecting a true hypothesis to as low a value as desired; (2) the test must be so devised that it will reject the hypothesis tested when it is likely to be false."[2]
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