The importance of proving the null.
Psychol Rev
; 116(2): 439-53, 2009 Apr.
Article
em En
| MEDLINE
| ID: mdl-19348549
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?
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Funções Verossimilhança
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Teorema de Bayes
/
Pesquisa Comportamental
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article