![]() ![]() In statistics, every conjecture concerning the unknown probability distribution of a collection of random variables representing the observed data X, and the underlying random variable is continuous, then the probability distribution of the p-value is uniform on the interval. ![]() In 2016, the American Statistician 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." 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." Basic concepts 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. ![]() A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. In null-hypothesis significance testing, the p-value 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. ![]()
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