linguistic:voice_change
In null-hypothesis significance testing, the p {\displaystyle 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. 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. 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". 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".