# How do you handle multiple comparisons?

## How do you handle multiple comparisons?

Below, I’ll provide a brief overview of available correction procedures for multiple comparisons.

1. Bonferroni Correction. The most conservative of corrections, the Bonferroni correction is also perhaps the most straightforward in its approach.
2. Sidak Correction.
3. Holm’s Step-Down Procedure.
4. Hochberg’s Step-Up Procedure.

How do you correct multiple hypothesis?

Bonferroni Correction method is simple; we control the α by divide it with the number of the testing/number of the hypothesis for each hypothesis. If we make it into an equation, the α Bonferroni is the significant α divided by m (number of hypotheses).

### Why do we do multiple test corrections?

Multiple testing correction adjusts the individual p-value for each gene to keep the overall error rate (or false positive rate) to less than or equal to the user-specified p-value cutoff or error rate.

How do you correct multiple t tests?

If you wish to make a Bonferroni multiple-significance-test correction, compare the reported significance probability with your chosen significance level, e.g., . 05, divided by the number of t-tests in the Table. According to Bonferroni, if you are testing the null hypothesis at the p≤.

#### What is the problem with running multiple t-tests?

Why not compare groups with multiple t-tests? Every time you conduct a t-test there is a chance that you will make a Type I error. This error is usually 5%. By running two t-tests on the same data you will have increased your chance of “making a mistake” to 10%.

What is the result of multiple testing?

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. In certain fields it is known as the look-elsewhere effect.