All decide which (if any) comparisons to label as "discoveries" and do so in a way that controls the false discovery rate to be less than a value Q you enter. Don't correct for multiple comparisons. Rejection of the null hypothesis leads to the conclusion that not all group means are the same. Multiple Comparisons Introduction. For example, it is possible that the ANOVA p-value can indicate that there are no differences between the means while the multiple comparisons output indicates that some means that are different.

Analysis of variance (ANOVA) techniques test whether a set of group means (treatment effects) are equal or not. The p-value in the ANOVA table and the multiple comparison results are based on different methodologies and can occasionally produce contradictory results. Each comparison stands alone. This result, however, does not provide further information on which group means are different. The FDR approach is not often used as a followup test to ANOVA, but there is no good reason for that. Analysis of variance: ANOVA, for multiple comparisonsThe ANOVA model can be used to compare the mean of several groups with each other, using a parametric method (assuming that the groups follow a Gaussian distribution).Proceed with the following example:The manager of a supermarket chain wants to see if the consumption in kilowatts of 4 stores between them are equal.

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