## How do you do the Bonferroni Holm correction?

For the Bonferroni correction, you simply multiply each p-value by the number of p-values (here by 3). For the Holm-Bonferroni, first you need to sort the p-values and then multiply the smallest by 3, then the second one by 2 etc.

## Should I use Holm or Bonferroni?

Alternatives and usage. The Holm–Bonferroni method is “uniformly” more powerful than the classic Bonferroni correction, meaning that it is always at least as powerful. . Thus, The Hochberg procedure is uniformly more powerful than the Holm procedure.

**What is Bonferroni correction example?**

For example, if we perform three statistical tests at once and wish to use α = . 05 for each test, the Bonferroni Correction tell us that we should use αnew = . 01667. Thus, we should only reject the null hypothesis of each individual test if the p-value of the test is less than .

### When should you use Bonferroni correction?

The Bonferroni correction is appropriate when a single false positive in a set of tests would be a problem. It is mainly useful when there are a fairly small number of multiple comparisons and you’re looking for one or two that might be significant.

### How do you do the Holm test?

Holm’s Test In Holm’s approach, we start with the smallest p-value (i = 1) and determine whether there is a significant result (i.e. p1 < α/(k–1+1) = α/k. If so we move on to the second test. We continue in this manner until we get a non-significant result (i.e. p1 ≥ α/(k–i+1)).

**When should you adjust for multiple comparisons?**

It is emphasized that adjustments for multiple testing are required in confirmatory studies whenever results from multiple tests have to be combined in one final conclusion and decision. In case of multiple significance tests a note on the error rate that will be controlled for is desirable.

## Is the Bonferroni correction really necessary?

Classicists argue that correction for multiple testing is mandatory. Epidemiologists or rationalists argue that the Bonferroni adjustment defies common sense and increases type II errors (the chance of false negatives).

## Should I use Bonferroni or Tukey?

For those wanting to control the Type I error rate he suggests Bonferroni or Tukey and says (p. 374): Bonferroni has more power when the number of comparisons is small, whereas Tukey is more powerful when testing large numbers of means.