# Question regarding significance level [duplicate]

Say, for instance, I'm estimating a model with about 5 million observations using linear regression or MLE. Given that the estimates are consistent, using the standard rule of rejecting the null on a 5% significance level will pretty much never make me entitled to reject the null (of the parameter being equal to zero), i.e. everything is significant.

I should probably use another significance level, which is smaller than 5%, and my question is if there is any literature on picking the right significance level, or how would you guys handle this problem? Should I simply stick with the 5% significance level?

Thanks.

## marked as duplicate by gung - Reinstate Monica♦, Nick Cox, Peter Flom - Reinstate Monica♦Jan 8 '14 at 10:25

1. The first (default) method to correct for the "multiple comparisons" problem is to use a Bonferroni adjustment to the p-value. The new $\alpha$ level is $\alpha^*=\frac{\alpha}{\#tests}$. Thus, divide the Type I error level of $\alpha=0.05$ by the number of tests you have.