Significance testing with no alternative hypothesis: A measure of surprise [Book Review]

Erkenntnis 70 (2):253 - 270 (2009)
Abstract
A pure significance test would check the agreement of a statistical model with the observed data even when no alternative model was available. The paper proposes the use of a modified p -value to make such a test. The model will be rejected if something surprising is observed (relative to what else might have been observed). It is shown that the relation between this measure of surprise (the s -value) and the surprise indices of Weaver and Good is similar to the relationship between a p -value, a corresponding odds-ratio, and a logit or log-odds statistic. The s -value is always larger than the corresponding p -value, and is not uniformly distributed. Difficulties with the whole approach are discussed.
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    References found in this work BETA
    J. V. Howard (1975). Computable Explanations. Mathematical Logic Quarterly 21 (1):215-224.
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