Bayesian methods for assessing importance of effects.

In experimental data analysis when it conies to assessing the importance of effects of interest, 2 situations are commonly met. In Situation 1, asserting largeness is sought: "The effect is large in the population." In Situation 2, asserting smallness is sought: "The effect is small in the population." In both situations, as is well known, conventional significance testing is far from satisfactory. The claim of this article is that Bayesian inference is ideally suited to making adequate inferences. Specifically, Bayesian techniques based on "noninformative" priors provide intuitive interpretations and extensions of familiar significance tests. The use of Bayesian inference for assessing importance is discussed elementarily by comparing 2 treatments, then by addressing hypotheses in complex analysis of variance designs.

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