How do you interpret Bonferroni multiple comparisons?

How do you interpret Bonferroni multiple 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.

What is the best correction for multiple comparisons?

The most conservative of corrections, the Bonferroni correction is also perhaps the most straightforward in its approach. Simply divide α by the number of tests (m). However, with many tests, α* will become very small. This reduces power, which means that we are very unlikely to make any true discoveries.

What are multiple comparison methods?

Multiple comparison methods (MCMs) are designed to investigate differences between specific pairs of means or linear combinations of means. This provides the information that is of most use to the researcher.

What is the purpose of doing a multiple comparison?

The purpose of most multiple-comparisons procedures is to control the “overall significance level” for some set of inferences performed as a follow-up to ANOVA.

How does Bonferroni method work?

Bonferroni designed his method of correcting for the increased error rates in hypothesis testing that had multiple comparisons. Bonferroni’s adjustment is calculated by taking the number of tests and dividing it into the alpha value.

What is the major problem of multiple comparisons?

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. The more inferences are made, the more likely erroneous inferences become.

What is not correct for multiple comparisons?

Some statisticians recommend never correcting for multiple comparisons while analyzing data (1,2). Instead report all of the individual P values and confidence intervals, and make it clear that no mathematical correction was made for multiple comparisons. This approach requires that all comparisons be reported.

How do you use Bonferroni correction?

Applying the Bonferroni correction, you’d divide P=0.05 by the number of tests (25) to get the Bonferroni critical value, so a test would have to have P<0.002 to be significant. Under that criterion, only the test for total calories is significant.

How does Bonferroni correction work?

To perform the correction, simply divide the original alpha level (most like set to 0.05) by the number of tests being performed. The output from the equation is a Bonferroni-corrected p value which will be the new threshold that needs to be reached for a single test to be classed as significant.

How many comparisons can be made?

If there are only two means, then only one comparison can be made. If there are 12 means, then there are 66 possible comparisons….