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There is a third way of implementing probability models and practicing. Thisis to answer questions put in terms of observables. This eliminates frequentisthypothesis testing and Bayes factors and it also eliminates parameterestimation. The Third Way is the logical probability approach, which is to makestatements $\Pr(Y \in y | X,D,M)$ about observables of interest $Y$ takingvalues $y$, given probative data $X$, past observations (when present) $D$ andsome model (possibly deduced) $M$. Significance and the false idea thatprobability models show causality are no more, and in their place areimportance and relevance. Models are built keeping on information that isrelevant and important to a decision maker (and not a statistician). All modelsare stated in publicly verifiable fashion, as predictions. All models mustundergo a verification process before any trust is put into them.

The Third Way Of Probability & Statistics: Beyond Testing and Estimation To Importance, Relevance, and Skill