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Is it just me, or does that table look like req.sig = (intended significance)/(#hypoth) ? Which is kind of a simpler formula. Edit: Following your link,

• timr 15y ago
  The Dunn-Sidak correction is preferred over the Bonferroni correction -- it's much less conservative, and will allow the finding of significance in situations that Bonferroni would miss. Another thing: depending on how your problem is structured, it might be a bit confusing to think of these as corrections for the number of hypotheses. I like to think of them in terms of the number of planned/unplanned comparisons that are being performed: you can do an experiment with a single stated hypothesis, yet still need to use these corrections if you perform "unplanned" comparisons using the data (a.k.a "data mining", or "a data fishing expedition") later on. This is a really great summary of planned vs. unplanned comparisons and why they matter: http://udel.edu/~mcdonald/statanovaplanned.html http://udel.edu/~mcdonald/statanovaplanned.html