Type I and type II errors

Concepts from statistical hypothesis testing

Type I error, or a false positive, is the incorrect rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the incorrect acceptance of a false null hypothesis. An analysis commits a Type I error when some baseline assumption is incorrectly rejected because of new, misleading information.

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