Second, the p value calculation method:
The left-hand test p value is the probability that the test statistic is less than or equal to the test statistic calculated according to the actual observed sample data at that time, that is, the p value.
The test p value on the right is the probability that the test statistic is greater than or equal to the test statistic calculated according to the actual observation sample data when μ=μ0, that is, the p value.
Two-sided test P value is the probability that the test statistic is greater than or equal to the test statistic calculated according to the actual observation sample data when μ=μ0, that is, P value.
Third, expand information:
About p value (source: network link)
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.
The p value is the probability that the sample observation result or more extreme result appears when the original hypothesis is true. If the p value is very small, it means that the probability of the original hypothesis is very small. If it appears, we have reason to reject the original hypothesis according to the principle of small probability. The smaller the p value, the more sufficient the reason for rejecting the original hypothesis. In short, the smaller the p value, the more significant the result. But whether the test result is "significant", "moderately significant" or "highly significant" needs to be solved by ourselves according to the size of P value and practical problems.