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  • Post-Hoc Tests - pelagicos. net
    P-Adjustment Methods • The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons • Less conservative corrections included by: o Holm (1979) ("holm") o Benjamini Hochberg (1995) ("BH" or "fdr")
  • 14. 6: Multiple Comparisons and Post Hoc Tests - Statistics . . .
    Here’s what the Holm correction does First, you sort all of your p-values in order, from smallest to largest For the smallest p-value all you do is multiply it by m, and you’re done However, for all the other ones it’s a two-stage process For instance, when you move to the second smallest p value, you first multiply it by m−1
  • Holm-Bonferroni Method: Step by Step - Statistics How To
    If you don’t know the p-values, run a test for each hypothesis before attempting to adjust FWER using the Holm-Bonferroni method Step 1: Order the p-values from smallest to greatest: H 4 = 0 005
  • Multiple comparisons and post-hoc tests — Learning statistics . . .
    Fig 133 also shows the Holm corrected p-values and, as you can see, the biggest p-value (corresponding to the comparison between Anxifree and the placebo) is unaltered At a value of 0 15, it is exactly the same as the value we got originally when we applied no correction at all
  • A general introduction to adjustment for multiple comparisons
    Holm method, in a stepwise way, computes the significance levels depending on the P value based rank of hypotheses For the i t h ordered hypothesis H ( i ) , the specifically adjusted significance level is computed:
  • Multiplicity adjusted P values from the Holm-Sidak method
    Here is a brief description of how the Holm multiple comparison test works: 1 If you are using the test as a followup to ANOVA, P values for each comparison are computed as they are for the Fisher's LSD test These are not corrected for multiple comparisons 2 The P values are ranked from smallest to largest
  • Holms and Hochbergs tests | Real Statistics Using Excel
    For these methods, we first rank the p-values of the k tests from smallest to largest p1 ≤ p2 ≤ … ≤ pk We then divide the nominal significance level α by the rank to create a significance level αi = α (k–i+1) for each test





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