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Power analysis is a procedure used in the context of null hypothesis significance testing to ensure that significance tests demonstrate sufficient power. The power of a study refers to the probability of rejecting a false null hypothesis, that is, the probability of a correct decision when the null hypothesis is false. Best practices in significance testing require conducting a power analysis to increase the likelihood of an accurate outcome.

By the 1950s, Ronald Fisher’s significance testing was the dominant method of drawing statistical conclusions about populations from samples in psychology. However, Jerzy Neyman and Egon Pearson noted that the results of a significance test are likely to be inaccurate if the null hypothesis is false. They introduced power analysis as a correction. In the context ...

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