Due to the high chance of multiple testing - I would like to know: > > 1. how one may correct for the same in Stata using Bonferroni's correction and You can do so by rejecting the null hypotheses if the p-value is less than a/k instead of a, whereby a is the significance level of your choice --- typically .05 --- and k is the number of tests. The Bonferroni correction is only one way to guard against the bias of repeated testing effects, but it is probably the most common method and it is definitely the most fun to say. 2 The Bonferroni correction The Bonferroni correction sets the signi cance cut-o at =n. Post-hoc pairwise comparisons are commonly performed after significant effects when there are three or more levels of a factor. The Bonferroni method is a simple technique for controlling the overall probability of a false significant result when multiple comparisons are to be carried out. It seems journals are considering Bonferroni adjustment for p-values of terms within a multiple regression model. Right now, > it's testing all combinations, and I'm afraid that this is going to > result in a Bonferroni OVERcorrection due to the inflated DF. The Bonferroni correction is named after Italian mathematician Carlo Emilio Bonferroni for its use of Bonferroni inequalities. I have a question about bonferroni correction that is slightly different from previous posts. Simply, the Bonferroni correction, also known as the Bonferroni type adjustment, is one of the simplest methods use during multiple comparison testing.

What is the Bonferroni correction method? I've been through every note I've ever taken, all my books, and a lot of the internet, and I'm still not sure about the Bonferroni correction. The Bonferroni correction tends to be a bit too conservative. For example, in the example above, with 20 tests and = 0:05, you’d only reject a null hypothesis if the p-value is less than 0.0025. by controlling false positives you're drastically increasing the number of false negatives). Ask Question ... One way to mitigate this risk is to use a Bonferroni Correction (BC), where you replace $\alpha$ with $$\dfrac{\alpha}{1000}$$ where 1000 is the number of tests that you run. Apply a correction to account for the number of multiple comparisons you are performing. You want to not call noise signal. Due to the high chance of multiple testing - I would like to know: > > 1. how one may correct for the same in Stata using Bonferroni's correction and You can do so by rejecting the null hypotheses if the p-value is less than a/k instead of a, whereby a is the significance level of your choice --- typically .05 --- … For instance, to obtain the Bonferroni adjusted p -value, multiply the uncorrected p -value by the total number of comparisons. FDR and Bonferroni corrections and Logistic Regression / Classification in High Dimensional Space.
See the "Methods and Formulas" section of [R] oneway for the appropriate correction. Together, I would have to run approximately 40 regression analyses across the 3 conditions for each group.