The Bonferroni Correction is the simplest, the most understandable, and the most extreme way of correcting for multiple statistical tests.
You take your ‘significance’ level and divide by the number of tests you are doing.
So if you have set ‘significance’ at 0.05, and do 5 different statistical tests, to be actually sure that your “rejection of the null hypothesis” (aka – it’s significant! it works!), you need to see the result to be
0.05 / 5 = 0.01 …. so …
p <0.01 before you can really call it a ‘significant’ result.
(It’s a bit harsh. It probably makes you make too many type II errors.)
(You can also apply it to correct confidence intervals. Do five tests, for the ‘real’ 95% confidence interval for each one, you need to calculate the 99% confidence interval.)