# p-value

## Function of the observed sample results / From Wikipedia, the free encyclopedia

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In null-hypothesis significance testing, the **$p$-value**^{[note 1]} is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct.^{[2]}^{[3]} A very small *p*-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Even though reporting *p*-values of statistical tests is common practice in academic publications of many quantitative fields, misinterpretation and misuse of p-values is widespread and has been a major topic in mathematics and metascience.^{[4]}^{[5]} In 2016, the American Statistical Association (ASA) made a formal statement that "*p*-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone" and that "a *p*-value, or statistical significance, does not measure the size of an effect or the importance of a result" or "evidence regarding a model or hypothesis."^{[6]} That said, a 2019 task force by ASA has issued a statement on statistical significance and replicability, concluding with: "*p*-values and significance tests, when properly applied and interpreted, increase the rigor of the conclusions drawn from data."^{[7]}