Both t value and p value are used to judge whether it is statistically significant. After the p value is the minimum alpha value that rejects the original hypothesis, write statistics and bring them into calculation, and then calculate the p value according to the distribution of statistics. P value is a parameter used to determine the hypothesis test results, and it can also be compared by using the rejection domain of distribution according to different distributions. It was first put forward by R A fisher. Fisher's specific practice

Assuming the value of a parameter, choose a test statistic. When the assumed parameter value is true, the distribution of this statistic should be completely known. Randomly select a sample from the research population, calculate the value of test statistics, and calculate the probability value or the observation value of significance level, that is, the probability that the test statistics are greater than or equal to the actual observation value under the assumption of truth.