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In a recent paper we have discussed certain general principles underlying the determination of the most efficient tests of statistical hypotheses, but the method of approach did not involve any detailed consideration of the question ofa prioriprobability. We propose now to consider more fully the bearing of the earlier results on this question and in particular to discuss what statements of value to the statistician in reaching his final judgment can be made from an analysis of observed data, which would not be modified by any change in the probabilitiesa priori. In dealing with the problem of statistical estimation, R. A. Fisher has shown how, under certain conditions, what may be described as rules of behaviour can be employed which will lead to results independent of these probabilities; in this connection he has discussed the important conception of what he terms fiducial limits. But the testing of statistical hypotheses cannot be treated as a problem in estimation, and it is necessary to discuss afresh in what sense tests can be employed which are independent ofa prioriprobability laws.

Both r, and rK lie between + 1 and-1. We shall not be concerned liere with ties among the u's or v's. The followiiig is a third coefficient which has been computed for all the sampling data and which we hope to consider later: (c) The Fisher-Ya.tes coefficient. Let ((i / n) be a so-called normal order statistic, i.e. the

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