# Misuse of p-values

## Misinterpretation of statistical significance / From Wikipedia, the free encyclopedia

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**Misuse of p-values** is common in scientific research and scientific education.

*p*-values are often used or interpreted incorrectly;[1] the American Statistical Association states that

*p*-values can indicate how incompatible the data are with a specified statistical model.[2] From a Neyman–Pearson hypothesis testing approach to statistical inferences, the data obtained by comparing the

*p*-value to a significance level will yield one of two results: either the null hypothesis is rejected (which however does not prove that the null hypothesis is

*false*), or the null hypothesis

*cannot*be rejected at that significance level (which however does not prove that the null hypothesis is

*true*). From a Fisherian statistical testing approach to statistical inferences, a low

*p*-value means

*either*that the null hypothesis is true and a highly improbable event has occurred

*or*that the null hypothesis is false.