Bonferroni Correction is a technique used to adjust the significance level of a statistical test when multiple tests are conducted. It helps to reduce the risk of making a Type I error (falsely rejecting the null hypothesis) by controlling the family-wise error rate (FWER). It involves dividing the chosen alpha level (e.g. 0.05) by the number of tests being conducted, thus creating a much stricter threshold for rejecting the null hypothesis.

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When you conduct a single statistical test to determine if two group means are equal, you typically compare the p-value of the test to some alpha (α) level like 0.05.

If the p-value of the test is less than 0.05, you reject the null hypothesis and conclude that the group means are different.

However, when you conduct multiple tests at once to compare several group means, there is a higher chance of committing a type I error and rejecting the null hypothesis when it’s actually true.

To control for this, you can perform a Bonferroni Correction and adjust the α level to be equal to:

α_adjusted = α/n

where:

α: The original α level
n: The total number of comparisons

Then, you only reject the null hypothesis of each individual test when the p-value is less than this adjusted α level.

To perform a Bonferroni Correction and calculate the adjusted α level, simply fill in the boxes below and then click the “Calculate” button.

Original α Level

Number of Comparisons (n)

Adjusted α: 0.01250

Interpretation: If you conduct 4 comparisons, only reject the null hypothesis of each comparison if it has a p-value less than 0.01250.

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What is Bonferroni Correction?

Requst a

Scale

Related terms:

Requst a

Scale