How does the Bonferroni correction adjust p-values?

The calculator multiplies each original p-value by the total number of comparisons being made. This adjusted p-value represents the threshold needed to maintain the overall Type I error rate across all tests.

When should I use the Bonferroni correction?

Use this method when performing multiple statistical comparisons on the same dataset to control the probability of finding at least one false-positive result, particularly in fields like psychology, biology, and economics.

What happens if my corrected p-value exceeds the significance level?

If the corrected p-value is greater than your chosen alpha level (commonly 0.05), the result is not considered statistically significant after accounting for multiple comparisons, and you should not reject the null hypothesis.

Can the Bonferroni correction be too strict?

Yes, when the number of comparisons is large, the correction can become overly conservative, making it harder to detect true effects and increasing the risk of Type II errors (failing to reject a false null hypothesis).

StatisticsFree Tool

Bonferroni Correction Calculator

Provided byOmni Calculatoromnicalculator.com

Our Bonferroni correction calculator helps you adjust p-values to account for multiple comparisons.

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Inference, regression, and statistical tests calculators

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About this tool

What Bonferroni Correction Calculator does

The Bonferroni Correction Calculator on Omni Calculator adjusts raw p-values by multiplying them with the number of statistical comparisons, producing corrected p-values that control the overall Type I error rate. Users receive a single adjusted p-value and a comparison against their chosen significance level, indicating whether results remain statistically significant after correction. The tool is designed for researchers and analysts who need to interpret multiple hypothesis tests without inflating false-positive rates. Beyond the core calculation, the page explains what the Bonferroni correction is, when to apply it, and provides practical examples to guide proper usage in fields like psychology, biology, and economics. It also links to related tools for power analysis and critical value determination, offering a broader statistical workflow within the same interface.

Step by step

How to use the Omni Calculator Bonferroni Correction Calculator

1
Enter the original p-value from your statistical test
2
Input the total number of comparisons or hypothesis tests being performed
3
The calculator multiplies the p-value by the number of tests to generate a corrected p-value
4
Compare the corrected p-value to your significance level (alpha) to determine if the result remains significant

Is it right for you

Best for

Researchers and data analysts conducting multiple simultaneous hypothesis tests who need to control the family-wise error rate and avoid false-positive conclusions.

Limitations

Assumes all tests are independent, which may not hold in all study designs
Can be overly conservative when the number of comparisons is large, increasing the risk of Type II errors
Does not provide confidence intervals or effect size information, only adjusted p-values

Questions

Bonferroni Correction Calculator FAQ

How does the Bonferroni correction adjust p-values?: The calculator multiplies each original p-value by the total number of comparisons being made. This adjusted p-value represents the threshold needed to maintain the overall Type I error rate across all tests.
When should I use the Bonferroni correction?: Use this method when performing multiple statistical comparisons on the same dataset to control the probability of finding at least one false-positive result, particularly in fields like psychology, biology, and economics.
What happens if my corrected p-value exceeds the significance level?: If the corrected p-value is greater than your chosen alpha level (commonly 0.05), the result is not considered statistically significant after accounting for multiple comparisons, and you should not reject the null hypothesis.
Can the Bonferroni correction be too strict?: Yes, when the number of comparisons is large, the correction can become overly conservative, making it harder to detect true effects and increasing the risk of Type II errors (failing to reject a false null hypothesis).

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