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I had planned to create a Real Statistics function that would do all of this for you for the previous release, but I ran out of time. Note that this approach uses harmonic interpolation, as explained on the webpage I can iterate this “divide and conqueror” approach to whatever degree of accuracy I desire. Since DCRIT(10,20.02) = 3.2265, which is higher than 3.2, I need a value higher than. I can make this more precise by using the DCRIT function. Thus if the test statistic is 3.2, then I know that the p-value is somewhere between. From the table I see that the critical values at. The best I can do at present is make an estimate based on the Table of Critical Values for Dunnett’s Test as shown inĮ.g. General pairwise tests (Bonferroni, Dunn-Sidàk, Holm, Hochberg, Benjamini-Hochberg, Benjamini-Yekutieli).Hsu’s MCB test is useful when you only want to make comparisons with the group with either the highest or lowest meanĬlick on any of the following post-hoc tests for further information:.Dunnett’s test is useful when you only want to make comparisons with a single control group.Games-Howell is useful when uncertain about whether population variances are equivalent.Benjamini-Hochberg is best when sample sizes are very different or where there are a very large number of tests.has more power) for comparing all pairs of means, but should not be used when group sizes are different It is a good choice for comparing large numbers of means Tukey’s test is usually the safe choice.We also describe the Scheffé test, which can be used for non-pairwise comparisons. We also describe extensions to Tukey’s HSD test (Tukey-Kramer and Games and Howell) where the sample sizes or variances are unequal. These tests are designed only for pairwise comparisons (i.e. More useful tests are Tukey’s HSD and REGWQ. For an experiment-wise error of α we need to use α/ m as the alpha for each test (Bonferroni) or 1 – (1 – α) 1/ m (Dunn/ Sidák). For k groups, this results in m = C( k, 2) = k( k–1)/2 tests. Although the Bonferroni and Dunn/Sidák correction factors can be used, since we are considering unplanned tests, we must assume that all pairwise tests will be made (or at least taken into account). Since multiple (usually pairwise) comparisons are performed, a key objective of all these tests is to control familywise error. Fortunately, there are criteria to follow that makes one test more appropriate than another. You should select one of these tests, stick with it, possibly using another test to gain some further insight. Although many tests are available, it is important to avoid the temptation to perform multiple tests a select the results that are most favorable to whatever you are trying to prove. We will review the most useful tests here. A number of unplanned comparisons are available.