Bonferronikorrekció

Bonferronikorrekció, also known as the Bonferroni correction, is a statistical method used to control the family-wise error rate when performing multiple hypothesis tests. The family-wise error rate is the probability of making at least one Type I error (falsely rejecting a true null hypothesis) across a set of tests. When many tests are conducted simultaneously, the chance of a Type I error increases substantially compared to conducting a single test.

The Bonferroni correction addresses this by adjusting the significance level (alpha) for each individual test. The original alpha level, typically set at 0.05, is divided by the total number of hypotheses being tested. For example, if you are performing 10 hypothesis tests, the new significance level for each individual test becomes 0.05 / 10 = 0.005. A result is then only considered statistically significant if its p-value is less than this adjusted alpha level.

While effective in controlling the family-wise error rate, the Bonferroni correction is known for being conservative. This means it can increase the probability of making a Type II error (failing to reject a false null hypothesis), especially when a large number of tests are performed. Other methods for multiple comparison correction exist, such as the Holm-Bonferroni method or the False Discovery Rate (FDR) control, which may offer a better balance between controlling Type I and Type II errors depending on the research context.

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