What is Bonferroni correction for multiple comparisons?

The Bonferroni test is a multiple-comparison correction used when several dependent or independent statistical tests are being performed simultaneously. The reason is that while a given alpha value may be appropriate for each individual comparison, it is not appropriate for the set of all comparisons.

How do you do the Bonferroni method?

To get the Bonferroni corrected/adjusted p value, divide the original α-value by the number of analyses on the dependent variable.

How do you interpret Bonferroni pairwise comparisons?

Bonferroni’s method provides a pairwise comparison of the means. To determine which means are significantly different, we must compare all pairs. There are k = (a) (a-1)/2 possible pairs where a = the number of treatments. In this example, a= 4, so there are 4(4-1)/2 = 6 pairwise differences to consider.

When should I 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 change the p-value for multiple comparisons?

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).

Why do you use a Bonferroni correction?

The Bonferroni correction is used to reduce the chances of obtaining false-positive results (type I errors) when multiple pair wise tests are performed on a single set of data. Put simply, the probability of identifying at least one significant result due to chance increases as more hypotheses are tested.

How do you analyze pairwise comparisons?

Pairwise Comparison Steps:

  1. Compute a mean difference for each pair of variables.
  2. Find the critical mean difference.
  3. Compare each calculated mean difference to the critical mean.
  4. Decide whether to retain or reject the null hypothesis for that pair of means.