False discovery rate calculator
Paste p values from a family of tests and choose the target false discovery rate q.
Ranked tests
| Rank | Input position | p | Critical (i/m)q | Adjusted p | Rejected |
|---|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||||
How to use this page
- Paste every p value from one test family.
- Set the target FDR q, commonly 0.05.
- Read the rejection count, cutoff and adjusted p values.
What this page does not do: The standard BH procedure controls FDR under independence or certain positive dependence structures. Other dependence patterns may require Benjamini–Yekutieli or a domain-specific model.
Further reading
The Benjamini–Hochberg procedure limits the expected proportion of false discoveries among rejected hypotheses. It ranks the p values, compares them with (i/m)q and selects the largest rank that meets the condition.
Adjusted p values are calculated backwards with a monotonicity constraint, so each hypothesis can be compared directly with q.
Frequently asked questions
How does FDR differ from Bonferroni?
Bonferroni controls the risk of at least one family-wise false positive, while BH controls the expected false-discovery proportion.
Does input order matter?
It does not change the decisions. The table retains each input position so you can map results back to tests.
See also
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.
Adjusted R-squared calculator
Convert R² to adjusted R² using the number of observations and predictors in a regression model.
Beta distribution calculator
Calculate beta density, CDF, upper tail and quantile together with its mean and variance.