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• W2104128511 startingPage "439" @default.
• W2104128511 abstract "Null hypotheses are simple, precise, and theoretically important. Conventional statistical analysis cannot support them; Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative, because the vaguer the alternative is (the more it spreads out the unit mass of prior probability), the more the null is favored. A general solution is a sensitivity analysis: Compute the odds for or against the null as a function of the limit(s) on the vagueness of the alternative. If the odds on the null approach 1 from above as the hypothesized maximum size of the possible effect approaches 0, then the data favor the null over any vaguer alternative to it. The simple computations and the intuitive graphic representation of the analysis are illustrated by the analysis of diverse examples from the current literature. They pose 3 common experimental questions: (a) Are 2 means the same? (b) Is performance at chance? (c) Are factors additive?" @default.
• W2104128511 created "2016-06-24" @default.
• W2104128511 creator A5087434888 @default.
• W2104128511 date "2009-01-01" @default.
• W2104128511 modified "2023-10-02" @default.
• W2104128511 title "The importance of proving the null." @default.
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• W2104128511 doi "https://doi.org/10.1037/a0015251" @default.
• W2104128511 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2859953" @default.
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• W2104128511 hasPublicationYear "2009" @default.
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