Statistics & Probability

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Bonferroni Correction Calculator.

Adjust a p-value and significance threshold for multiple comparisons.

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01

Set your values

Results update as you type.

adjustedPValue: 0.05

adjustedPValue

0.050000
adjustedAlpha: 0.01

adjustedAlpha

0.010000
significantAfterCorrection: true

significantAfterCorrection

true

Results update automatically as you type.

Use Cases

Multiple hypothesis testing

When conducting multiple statistical tests simultaneously, use the Bonferroni correction to control the family-wise error rate and reduce false positives.

Example: Testing 10 genes for association with a disease; corrected alpha = 0.05/10 = 0.005.

Post-hoc analysis in ANOVA

After an ANOVA, when making pairwise comparisons between groups, apply the Bonferroni correction to adjust p-values for the number of comparisons.

Example: Comparing 4 group means pairwise (6 comparisons); corrected alpha = 0.05/6 ≈ 0.0083.

Frequently Asked Questions

What is the Bonferroni correction?
The Bonferroni correction is a statistical method used to counteract the problem of multiple comparisons. It adjusts the significance level (alpha) by dividing it by the number of comparisons, reducing the chance of false positives (Type I errors).
How do I use this calculator?
Enter your original p-value, the number of comparisons you are making, and your desired alpha level (usually 0.05). The calculator will compute the Bonferroni-corrected alpha and tell you whether your p-value is significant after correction.
What is the corrected alpha?
The corrected alpha is the original alpha divided by the number of comparisons. For example, if alpha is 0.05 and you have 5 comparisons, the corrected alpha is 0.01. Your p-value must be less than this corrected alpha to be considered significant.

Tips & Common Mistakes

Tips

  • Always specify the number of comparisons you actually performed, not the number you intended.
  • If you have many comparisons, the Bonferroni correction can be very conservative; consider other methods like FDR if appropriate.
  • Use the corrected alpha as your threshold for significance, not the original alpha.
  • Remember that the Bonferroni correction assumes independence of tests; if tests are correlated, it may be overly strict.

Common Mistakes to Avoid

  • Using the original alpha (e.g., 0.05) instead of the corrected alpha when interpreting p-values.
  • Forgetting to count all comparisons, including post-hoc or exploratory tests.
  • Applying the correction to a single test or when comparisons are not independent, leading to overly conservative results.

Last updated: August 13, 2026