## About False Discovery Rate Calculator (Formula)

The False Discovery Rate (FDR) is a statistical measure that gives an estimate of the proportion of false positive results in a set of hypotheses tests. In other words, it is a measure of the rate at which the null hypotheses are incorrectly rejected.

FDR is important in multiple hypothesis testing, where multiple tests are performed simultaneously, as it helps to control the number of false positive results and maintain a reasonable level of confidence in the results.

The formula for False Discovery Rate (FDR) is given by:

**FDR = (Number of False Discoveries) / (Number of Tests Performed) * 100**

where:

- FDR is the False Discovery Rate, expressed as a percentage.
- Number of False Discoveries is the number of null hypotheses that were incorrectly rejected (i.e., the number of false positive results).
- Number of Tests Performed is the total number of hypotheses tests that were performed.

The FDR formula can be used to calculate the proportion of false positive results in a set of hypotheses tests, by dividing the number of false positive results by the total number of tests performed and multiplying the result by 100 to express it as a percentage.

In general, a lower FDR indicates a higher level of confidence in the results, as it means that a lower proportion of the rejections are false positives. Conversely, a higher FDR indicates that a larger proportion of the rejections are false positives and that the results may not be as trustworthy.

The FDR formula is a useful tool for anyone who performs multiple hypothesis tests, as it helps to control the number of false positive results and maintain a reasonable level of confidence in the results.