# Why don't we compute a p-value for p-value being below 0.05?

For the purpose of this post, accept that there is some critical threshold below which a finding is declared to be statistically significant.

The p-value is a statistic because it is a function of the data.

Statistics are random variables. We can put confidence intervals around statistics.

Why don't we put confidence intervals around p-values? Or, equivalently, why don't we test the hypothesis that the observed p-value is below 0.05, say?

One answer could be that it would be hard to calculate analytically, but bootstrapping could solve that issue.

• p-value is a characteristic of a given sample, not an estimate of something in the population to be able to bear some error. So p-value is an exact number, not an interval. p-value is interval only in Monte Carlo testing - because in such a testing not full information from the sample is used to compute p. Mar 8 '13 at 5:56
• The p-value depends on the null hypothesis in addition to the data, whereas the data do not depend on the null hypothesis. Jun 19 '15 at 17:15
• Very relevant thread: stats.stackexchange.com/questions/254595 - see discussions in the comments. Aug 18 '17 at 16:30