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Abstract

The classical Analysis of Means (ANOM) is a statistical inferencing procedure and visualization tool to analyze means from experiments with fixed effects. It can serve as an alternative to the Analysis of Variance (ANOVA) procedure that has distinct advantages when determining which effects contributed to an overall test’s significant result. ANOM has been extended to handle numerous situations including robust procedures involving ranks. More recent advancements of this procedure allow one to handle both random, and mixed effect models. In this work, we discuss the recent developments on ANOM methods that are useful in practice, provide examples that illustrate their effectiveness, and discuss logistical issues with post hoc testing.

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