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It appears that you and they disagree by a factor of $\sqrt{2}$. If you look at the table rows with $k=2$, they have the familiar 1.96. R gives > round(qtukey(.95,5:2,Inf),2) [1] 3.86 3.63 3.31 2.77 > round(qtukey(.95,5:2,Inf)/sqrt(2),2) [1] 2.73 2.57 2.34 1.96 where the 3.858 matches what you found in the reference table, and the second row matches ...


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Is there any adjustment for multiple comparisons built into the p-values for individual regression coefficients? If not, is it prudent to apply an adjustment (Bonferronni, FDR etc) to the coefficient p-values? In short, yes, there can be. I usually use linear regression for everything, including designs that could be estimated with an ANOVA. I use R, so I ...


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(Partially from comments.) There is no indication in the results of the Tukey HSD test that group A is different than the other groups. The p values for each comparison are greater than 0.24. [e.g. 0.24, 0.29, 0.67]. Also, the confidence interval for each difference between groups includes 0. [e.g. B-A has limits of -44.8 and 12.96]. The residuals from ...


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Unfortunately, I cannot open your link as it expired. However, I believe, as POC mentioned, the easiest way to approach a Tukey-HSD test is to look at all the different variables in a table and then cross-check which variables are significant and vice versa. What program do you use? E.g. Let us imagine a very very simple dataset consisting of daily hours of ...


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When doing multiple comparisons, the alpha value (Type I error rate) is accumulated over all tests. One method to do multiple comparisons is Fisher's Least Significant Difference (LSD). Fisher's LSD computes the "least" or minimum value of difference required between two group means. If the difference between two group means is larger than this ...


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