What is a Bonferroni correction used for in statistical testing?

The Bonferroni correction is a statistical adjustment used to counteract the problem of multiple comparisons. When conducting several hypothesis tests simultaneously, the chance of committing a Type I error—rejecting a true null hypothesis—increases. The Bonferroni correction addresses this by adjusting the cut-off points for statistical significance to control for this multiplicity.

Specifically, this approach involves dividing the alpha level (the threshold for significance) by the number of comparisons being made. For instance, if you were conducting five tests and intended to maintain a significance level of 0.05, you would adjust your alpha for each individual test to 0.01 (0.05/5). This adjustment maintains the overall rate of Type I errors across all tests.

Adjusting cut-off points in this way is vital for ensuring that findings are not merely due to chance when multiple tests are conducted. Therefore, the function of the Bonferroni correction is essential for researchers who are looking to draw valid conclusions from their data while controlling for the increased risk associated with multiple comparisons.