Bonferronikorjausta

Bonferroni correction is a statistical method used to prevent false positives in multiple hypothesis testing. It was developed by Italian statistician Carlo Emilio Bonferroni in the 1930s. The method is based on the idea that the probability of at least one false positive result increases with the number of individual tests being performed.

In the context of multiple hypothesis testing, Bonferroni correction works by adjusting the significance level of each individual test. The adjusted significance level is calculated as the original significance level (typically 0.05) divided by the number of tests being performed. This adjusted significance level is then used to determine the p-value cutoff for rejecting the null hypothesis.

The Bonferroni correction assumes that the test statistics are independent and identically distributed. It is a conservative method, meaning that it tends to err on the side of caution by reducing the power to detect true effects. This can lead to a loss of sensitivity in detecting significant differences when testing multiple hypotheses.

Despite its limitations, the Bonferroni correction is widely used in various fields, including medicine, psychology, and biology. It is often used as a simple and conservative approach to account for multiple comparisons. However, its restrictive nature can sometimes lead to the omission of statistically significant results due to the adjusted p-value threshold. As a result, alternative methods, such as the Holm-Bonferroni method and the Benjamini-Hochberg procedure, have been developed to address the limitations of the Bonferroni correction.