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In statistics, the Holm–Bonferroni method, also called the Holm method or Bonferroni–Holm method, is used to counteract the problem of multiple comparisons. It is intended to control the family-wise error rate (FWER) and offers a simple test uniformly more powerful than the Bonferroni correction.
In statistics, the Bonferroni correction is a method to counteract the multiple comparisons problem.
Multiple testing correction refers to making statistical tests more stringent in order to counteract the problem of multiple testing. The best known such adjustment is the Bonferroni correction, but other methods have been developed.
The harmonic mean p-value (HMP) procedure provides a multilevel test that improves on the power of Bonferroni correction by assessing the significance of groups of hypotheses while controlling the strong-sense family-wise error rate.
In 1979, Holm proposed the Holm procedure, a stepwise algorithm for controlling the FWER that is at least as powerful as the well-known Bonferroni adjustment. This stepwise algorithm sorts the p -values and sequentially rejects the hypotheses starting from the smallest p -values.
Chi-square automatic interaction detection (CHAID) is a decision tree technique based on adjusted significance testing (Bonferroni correction, Holm-Bonferroni testing). The technique was developed in South Africa in 1975 and was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on this topic.
The Holm–Bonferroni method is a special case of a closed test procedure for which each intersection null hypothesis is tested using the simple Bonferroni test. As such, it controls the family-wise error rate for all the k hypotheses at level α in the strong sense.
Post hoc analysis. In a scientific study, post hoc analysis (from Latin post hoc, "after this") consists of statistical analyses that were specified after the data were seen.
Dunnett's test's calculation is a procedure that is based on calculating confidence statements about the true or the expected values of the differences , thus the differences between treatment groups' mean and control group's mean. This procedure ensures that the probability of all statements being simultaneously correct is equal to a specified ...
The currently most popular method to control this is to use the Bonferroni correction for the α-level. There are a number of alternative methods to control the α-level. One alternative, the Holm–Bonferroni method introduced by Sture Holm, considers the number of tests already finished when the ith test is performed.