What is a Bonferroni post hoc test?

What is a Bonferroni post hoc test?

A Bonferroni test is perhaps the simplest post hoc analysis. A Bonferroni test is a series of t-tests performed on each pair of groups. As we discussed earlier, the number of groups quickly grows the number of comparisons, which inflates Type I error rates.

When should Bonferroni be used?

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.

What is the difference between Bonferroni and Scheffe post hoc test?

Note that the Scheffe post-hoc test can be used whether or not the group sample sizes are equal. The Bonferroni post-hoc test should be used when you have a set of planned comparisons you would like to make beforehand.

What is the best post hoc test for correlation?

The Bonferroni is probably the most commonly used post hoc test, because it is highly flexible, very simple to compute, and can be used with any type of statistical test (e.g., correlations)—not just post hoc tests with ANOVA. The traditional Bonferroni, however, tends to lack power (Olejnik, Li, Supattathum, & Huberty, 1997).

What is a Bonferroni correction in statistics?

A Bonferroni Correction refers to the process of adjusting the alpha (α) level for a family of statistical tests so that we control for the probability of committing a type I error. The formula for a Bonferroni Correction is as follows: αnew = αoriginal / n.

How do you calculate Bonferroni’s adjustment?

Bonferroni’s adjustment is calculated by taking the number of tests and dividing it into the alpha value. Using the 5% error rate from our example, two tests would yield an error rate of 0.025 or (.05/2) while four tests would therefore have an error rate of .0125 or (.05/4). Notice that the error rate decreases as the sample size increases.