In a hypothesis test, a Type I error occurs when a true null hypothesis is rejected.
What is hypothesis test?
Hypothesis testing is a statistical procedure in which an analyst tests an assumption about a population parameter. The methodology used by the analyst is determined by the nature of the data and the purpose of the analysis.
Using sample data, hypothesis testing is used to evaluate the plausibility of a hypothesis. Such information could come from a larger population or from a data-generating process. In the following descriptions, the word "population" will be used for both of these cases.
A type I error (false-positive) occurs when an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs when the investigator fails to reject a null hypothesis that is actually false in the population.
Hence, in a hypothesis test, a Type I error occurs when a true null hypothesis is rejected.
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