Return to Answer

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(https://alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(https://alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.

The second issue, which arises even when using appropriate tests, is that you are making the false assumption that rejection of an omnibus null hypothesis means there must be at least one rejection of pairwise post hoc null hypothesis.

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(http://www.alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(https://alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(http://www.doyenne.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.

Your mistake is in choosing the Wilcoxon/Mann-Whitney rank-sum tests as your post hoc tests following the rejection of the Kruskal-Wallis. The appropriate pos hoc test is Dunn's test^* which properly (1) accounts for pooled variance assumed by the null hypothesis, and (2) uses the same ranks for your data as used in the construction of the Kruskal-Wallis test. The vanilla rank-sum tests entail separate estimates of variance for each pair-wise test, and ignore the rankings of the total data set as performed with a Kruskal-Wallis test.

Dunn's test is implemented for Stata in the dunntest package (within Stata type net describe dunntest, from(http://www.alexisdinno.com/stata)), and for R in the dunn.test package. Not sure about implementations in SAS.

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.

^* There are some far less used alternatives to Dunn's test including the Conover-Iman (like Dunn, but based on the t distribution, rather than the z distribution, implemented for Stata in the conovertest package, and for R in the conover.test package), and the Dwass-Steel-Citchlow-Fligner tests.