Multiple testing adjustment: Bonferroni, Holm, Chow-Denning, and Wilson (Excel) 

When testing for a lot of individual hypothesis at the same time, some of them can turn out significant and the nulls can be rejected due to random chance alone. This is very prominent in financial econometrics where the significance of many results can be suspected to originate from extensive data-mining. There are, however, statistical procedures that can be applied to account for family-wise error rate (FWER) and adjust for multiple testing. Today we are investigating four of the most commonly used: Bonferroni, Holm, Chow-Denning (also known as Dunn-Sidak), and Wilson harmonic mean p-value.
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