Post Hoc Tests > Benjamini-Hochberg Procedure

## What is the Benjamini-Hochberg Procedure?

The Benjamini-Hochberg Procedure is a powerful tool that decreases the false discovery rate.

Adjusting the rate helps to control for the fact that sometimes small p-values (less than 5%) happen by chance, which could lead you to incorrectly reject the true null hypotheses. In other words, the B-H Procedure helps you to avoid Type I errors (false positives).

A p-value of 5% means that there’s only a 5% chance that you would get your observed result *if* the null hypothesis were true. In other words, if you get a p-value of 5%, it’s highly unlikely that your null hypothesis is not true and should be thrown out. But it’s only a probability–many times, true null hypotheses are thrown out just because of the randomness of results.

**A concrete example: **Let’s say you have a group of 100 patients who you know are free of a certain disease. Your null hypothesis is that the patients are free of disease and your alternate is that they *do* have the disease. If you ran 100 statistical tests at the 5% alpha level, **roughly 5% of results would report as false positives.**

There’s not a lot you can do to avoid this: **when you run statistical tests, a fraction will always be false positives.** However, running the B-H procedure will decrease the number of false positives.

## How to Run the Benjaminiâ€“Hochberg procedure

- Put the individual p-values in ascending order.
- Assign ranks to the p-values. For example, the smallest has a rank of 1, the second smallest has a rank of 2.
- Calculate each individual p-value’s Benjamini-Hochberg critical value, using the formula (i/m)Q, where:
- i = the individual p-value’s rank,
- m = total number of tests,
- Q = the false discovery rate (a percentage, chosen by you).

- Compare your original p-values to the critical B-H from Step 3; find the largest p value that is smaller than the critical value.

As an example, the following list of data shows a **partial list of results from 25 tests **with their p-values in column 2. The list of p-values was ordered (Step 1) and then ranked (Step 2) in column 3. Column 4 shows the calculation for the critical value with a false discovery rate of 25% (Step 3). For instance, column 4 for item 1 is calculated as (1/25) * .25 = 0.01:

The bolded p-value (for Children) is the highest p-value that is also smaller than the critical value: .042 < .050. **All **values above it (i.e. those with lower p-values) are highlighted and considered significant, even if those p-values are lower than the critical values. For example, Obesity and Other Health are individually, not significant when you compare the result to the final column (e.g. .039 > .03). However, with the B-H correction, they are considered significant; in other words, you would reject the null hypothesis for those values.

## References

Beyer, W. H. CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002.

Agresti A. (1990) Categorical Data Analysis. John Wiley and Sons, New York.

Kotz, S.; et al., eds. (2006), Encyclopedia of Statistical Sciences, Wiley.

Vogt, W.P. (2005). Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences. SAGE.

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