I have theoretical knowledge of why we should use bonferroni; however, unfortunately not how.

I am conducting multiple t.tests, sometimes within the main data (number of t.tests = 5, number of main tests/regression = 2), and sometimes within the split data by gender (number of tests = 2).

I am wondering then whether I should conduct bonferroni separately for the main data and the split data.

using R, finding the adjusted p using bonferroni for the main data is then:

p.adjust(c(0.0012, 0.005, 0.0464, 0.003, 0.0321, 0.001, 0.002), method = "bonferroni")

and the same for the split data would then be:

p.adjust(c(0.0464, 0.0321), method = "bonferroni")

Am I doing this correctly? Any help will be appreciated, thanks.

  • $\begingroup$ And am I understanding it correctly that it is generally not preferred to do bonferroni on exploratory analyses? Thanks! $\endgroup$ – user240313 May 29 '19 at 18:03

The Bonferroni correction is an adjustment to the threshold of significance (i.e., alpha) for your p-value. This correction is made to account for inflated Type I error (the higher the chance for a false positive). It is a very straight forward statistic -- you simply divide your p-value by the number of independent hypothesis tests that you have conducted. If you are testing 9 hypotheses, and the standard alpha value in your field is .05, your Bonferroni corrected alpa is .05/9 = .006. This means any p-value you have that is above .006 (but below .05) is treated with suspicion.

Thus, your first step is to really specify how many hypotheses you are testing.

I hope that helps!

  • $\begingroup$ Yes that helps enormously, thanks!! $\endgroup$ – user240313 May 29 '19 at 18:47

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