David Robinson visualizes and interprets different p-value histograms:
So you’re a scientist or data analyst, and you have a little experience interpreting p-values from statistical tests. But then you come across a case where you have hundreds, thousands, or even millions of p-values. Perhaps you ran a statistical test on each gene in an organism, or on demographics within each of hundreds of counties. You might have heard about the dangers of multiple hypothesis testing before. What’s the first thing you do?
Make a histogram of your p-values. Do this before you perform multiple hypothesis test correction, false discovery rate control, or any other means of interpreting your many p-values. Unfortunately, for some reason, this basic and simple task rarely gets recommended (for instance, the Wikipedia page on the multiple comparisons problem never once mentions this approach). This graph lets you get an immediate sense of how your test behaved across all your hypotheses, and immediately diagnose some potential problems. Here, I’ll walk you through a basic example of interpreting a p-value histogram.
It’s a fun read and informative as well.