Significance testing with no alternative hypothesis: A measure of surprise
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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Ken Levy (2009). The Solution to the Surprise Exam Paradox. Southern Journal of Philosophy 47 (2):131-158.
Davis Baird (1984). Tests of Significance Violate the Rule of Implication. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:81 - 92.
Franz Huber (2008). Milne's Argument for the LogāRatio Measure. Philosophy of Science 75 (4):413-420.
Joseph F. Hanna (1966). A New Approach to the Formulation and Testing of Learning Models. Synthese 16 (3-4):344 - 380.
G. William Moore, Grover M. Hutchins & Robert E. Miller (1986). A New Paradigm for Hypothesis Testing in Medicine, with Examination of the Neyman Pearson Condition. Theoretical Medicine and Bioethics 7 (3).
Emiliano Lorini & Cristiano Castelfranchi (2007). The Cognitive Structure of Surprise: Looking for Basic Principles. Topoi 26 (1).
Siu L. Chow (1998). The Null-Hypothesis Significance-Test Procedure is Still Warranted. Behavioral and Brain Sciences 21 (2):228-235.
Joseph Y. Halpern & Yoram Moses (1986). Taken by Surprise: The Paradox of the Surprise Test Revisited. Journal of Philosophical Logic 15 (3):281 - 304.
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