Get 7 Corrected (p

Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter will result in fewer false positives.

The Bonferroni corrected p-values handle the multiple testing problem by controlling the 'family-wise error rate': the probability of making at least one false positive call. They are calculated by multiplying the original p-values by the number of tests performed.

One of the reasons you have identical adjusted P-values is that many P-values reached 1/nperm limit. Increasing nperm will get you higher resolution and there will be smaller number of identical adjusted P-values.

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.

Bonferroni and Holm adjusted p-values provide upper bounds for rejection probabilities under the null hypothesis. They are on the same scale as probabilities, so having a value larger than 1 is meaningless. They have the same interpretation as p-values.

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. Here is a simple way to think about it. You perform multiple comparisons twice.