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How to adjust p values for multiple testing?
The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values).
What is adjust p value?
The adjusted P value is the smallest familywise significance level at which a particular comparison will be declared statistically significant as part of the multiple comparison testing. A separate adjusted P value is computed for each comparison in a family of comparisons.
Why do you adjust p values?
A p-value adjustment is necessary when one performs multiple comparisons or multiple testing in a more general sense: performing multiple tests of significance where only one significant result will lead to the rejection of an overall hypothesis.
Why do I get different p values in p.adjust?
When I create a list using the p-values and then use that list in the p.adjust function, I get different adjusted p-values then when I simply run each p-value in the p.adjust function independently. Can anyone help to explain why this might be the case and which way is giving the correct adjusted p-values? My code is below with the outputs.
Why does post hoc adjust different values when using a list vs individual p-values?
This should explain why using a single p-value and a preset value of n gives a different result: I think the intuition is that the BH method considers the distribution of pvalues not just the values individually and it adjusts them accordingly. Thanks for contributing an answer to Cross Validated!
How is the adjusted p value for a study calculated?
The adjusted P value for each comparison depends on all the data, not just the data in the two groups that P value compares. If you added one more comparison to the study (or took one away), all the adjusted P values would change.
Which is the correct way to adjust the function P?
A numeric vector of corrected p-values (of the same length as p, with names copied from p ). The adjustment methods include the Bonferroni correction ( “bonferroni”) in which the p-values are multiplied by the number of comparisons.