2018年6月9日 · Tukey HSD, Dunn-Sidak correction, and Dunn test are all different things, and aren't really alternatives to one another. Tukey HSD is a method to compare among means of multiple groups, as would be common as a post-hoc test after a one-way anova. It is limited by requirements for normality and homoscedasticity.

For the normal distributions I used ANOVA to analyze the differences (percent change) between the results of treatments, then used the Tukey HSD to test the significance of differences between pairs of results. For the non-normally distributed data I used the Wilcoxon/Kruskal-Wallis test.

2023年8月10日 · Tukey's HSD test is a parametric method that uses the distribution of the range of the (standardized) differences in the means. The permutation approach would be to compute this distribution of the range based on permuted samples. It is a bit tricky to use the distribution obtained from permutations.

2023年3月16日 · This seemed to work fairly well at limiting our gathering of false-positives. However, in my current role, in some cases, I end up using Tukey HSD for multiple comparisons (it's especially useful when I wish to categorize groups). As I understand, Tukey HSD also accounts for multiple comparisons.

2020年3月29日 · With more than two groups, multiple comparisons can become an issue as multiple tests are performed. The Wikipedia page for Tukey's HSD test says Tukey's test accounts for multiple comparisons. Is there any adjustment for multiple comparisons built into the p-values for individual regression coefficients?

2017年8月4日 · The theory behind Tukey's HSD test. Hot Network Questions A family has a child.They adopt a girl as well.If one of the children becomes #1 in a women's race, what is the probability of the girl being adopted?

2012年7月30日 · It is not necessary to correct for multiple comparisons when using Tukey's HSD. The procedure was developed specifically to account for multiple comparison and maintains experiment-wise alpha at the specified level (conventionally .05). Page 210 of Maxwell and Delaney's book on experimental design has explanations and examples of the procedure.

A post hoc Tukey test showed that the future alone and future belonging groups differed significantly at p < .05; the misfortune control group was not significantly different from the other two groups, lying somewhere in the middle.

If the p-value is greater than 0.05, then there is no need to run post hoc tests such as the Tukey because you already know that there is no significant differences. I am sure that in this example, the p-value was greater than 0.05 for the anova test, which is why when you ran the post hoc Tukey test, no significant differences was observed.

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