MCA – a thing of the past?

All pair-wise Multiple Comparisons(MCA) is a well known collection of procedures for the stochastic ordering of means; which is a common research task. Classical methods rely on the assumption that the null hypothesis is true. Modern alternatives can be found in the Bayesian Statistics paradigm which abandons the Type 1 error notion. In particular, for problems that can be cast in the hierarchical modeling framework, a principled Bayesian approach relies on partial pooling and shrinkage. Technical arguments supporting this approach have been around for some time. An excellent working paper by Andrew Gleman on the topic presents an overview, simulation results and examples demonstrating the benefits in an applied setting. Suggestions on the use of R and other software is mentioned for implementation.

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