Bonferroni Adjustment:
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The Bonferroni adjustment is a method to counteract the problem of multiple comparisons by adjusting the significance level. It divides the original alpha level by the number of tests being performed to maintain the overall Type I error rate.
The calculator uses the Bonferroni equation:
Where:
Explanation: This simple division ensures that the family-wise error rate (probability of making one or more false discoveries) remains at the original alpha level.
Details: Without adjustment, conducting multiple tests increases the chance of false positives. The Bonferroni correction is conservative but effective for controlling family-wise error rate.
Tips: Enter your original alpha level (usually 0.05) and the number of tests you're performing. The calculator will output the new threshold for statistical significance.
Q1: When should I use the Bonferroni adjustment?
A: When conducting multiple hypothesis tests simultaneously and you want to maintain your overall Type I error rate.
Q2: Is the Bonferroni adjustment too conservative?
A: It can be, especially with many tests. Alternatives like the Holm-Bonferroni or false discovery rate methods may be more powerful.
Q3: What's a typical original alpha value?
A: 0.05 is standard, but some fields use 0.01 or other values depending on context.
Q4: Does order of tests matter with Bonferroni?
A: No, unlike some stepwise methods, Bonferroni treats all tests equally regardless of order.
Q5: Can I use this for confidence intervals?
A: Yes, you can adjust confidence levels similarly (e.g., 95% CI becomes 99% for 5 tests).