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For example, to test two independent hypotheses on the same data at 0.05 significance level, instead of using a p value threshold of 0.05, one would use a stricter threshold of 0.025.

The Bonferroni correction is a safeguard against multiple tests of statistical significance on the same data falsely giving the appearance of significance, as 1 out of every 20 hypothesis-tests will appear to be significant at the α = 0.05 level purely due to chance.

It was developed by Italian mathematician Carlo Emilio Bonferroni.

A uniformly more powerful test procedure is the Holm-Bonferroni method, however current methods for obtaining confidence intervals for the Holm-Bonferroni method do not guarantee confidence intervals that are contained within those obtained using the

from Wikipedia

I am afraid I am getting confused with Bonferroni correction explained by Wikipedia so I hope to find some help here.

I have performed Wilcoxon's test on 12 variables of ten cases each, so I have 65 hypothesis to test. Which is the level of significance i have to choose using Bonferroni correction?

do i have to do 1/n so 1/65=0.015?

I am afraid I am getting confused with Bonferroni correction explained by Wikipedia so I hope to find some help here.

I have performed Wilcoxon's test on 12 variables of ten cases each, so I have 65 hypothesis to test. Which is the level of significance i have to choose using Bonferroni correction?

do i have to do 1/n so 1/65=0.015?

e.g. I want to be certain that my 65 hypotheses about a data set are correct 95% of the time, so each test should be tested at the 5%*1/65 or .077% confidence level to ensure that the failure of all the tests as whole is 65*.077% or 5%.

Wilcoxon's Test is non-parametric and more conservative than parametric methods already. Bonferroni correction adjusts parametric methods to account for more liberal use. Essentially, you just divide your significance level by the number of tests you run so that any one test is sufficiently stringent such that all your tests as a group are right (1-significance level)% of the time.

e.g. I want to be certain that my 65 hypotheses about a data set are correct 95% of the time, so each test should be tested at the 5%*1/65 or .077% confidence level to ensure that the failure of all the tests as whole is 65*.077% or 5%.

e.g. I want to be certain that my 65 hypotheses about a data set are correct 95% of the time, so each test should be tested at the 5%*1/65 or .077% confidence level to ensure that the failure of all the tests as whole is 65*.077% or 5%.