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Calculate False Discovery Rate (FDR) adjustments for multiple hypothesis testing with p-value upload and method selection.

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Sobre esta herramienta

This FDR (False Discovery Rate) calculator helps researchers and statisticians correct for multiple comparisons when conducting multiple hypothesis tests. Upload your p-values via CSV or enter them manually, then choose between the Benjamini-Hochberg (1995) or Benjamini-Yekutieli (2001) adjustment methods. The tool calculates adjusted p-values (q-values) that control the false discovery rate, helping you identify which findings remain statistically significant after correction. Results include a detailed table showing original p-values, adjusted p-values, and significance status at your chosen threshold.
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Cómo usar

  1. Enter p-values manually (comma-separated) or upload a CSV file with one p-value per line
  2. Set your significance threshold (default
  3. Choose adjustment method: Benjamini-Hochberg (independent tests) or Benjamini-Yekutieli (any dependency)
  4. Click 'Calculate FDR' to process
  5. View results showing adjusted p-values and which hypotheses remain significant
How to use calculator fdr

Frequently Asked Questions

Encuentre respuestas rápidas

What is False Discovery Rate (FDR)?
FDR is the expected proportion of false discoveries among all rejected hypotheses. Unlike family-wise error rate (FWER) which controls the probability of any false discovery, FDR is less conservative and more powerful for large-scale testing.
When should I use Benjamini-Hochberg vs Benjamini-Yekutieli?
Use Benjamini-Hochberg when your test statistics are independent or positively dependent. Use Benjamini-Yekutieli when there may be any dependency structure between tests, as it's more conservative but valid under arbitrary dependence.
What format should my CSV file have?
Your CSV should contain one p-value per line, with optional header row. Values should be between 0 and 1. Example: 0.001,0.023,0.45,0.0001
How are adjusted p-values interpreted?
Adjusted p-values (q-values) represent the minimum FDR at which the test would be declared significant. If q-value ≤ your threshold (e.g., 0.05), reject the null hypothesis while controlling FDR at that level.

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