Document Detail


The importance of proving the null.
MedLine Citation:
PMID:  19348549     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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?
Authors:
C R Gallistel
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Publication Detail:
Type:  Journal Article; Review    
Journal Detail:
Title:  Psychological review     Volume:  116     ISSN:  0033-295X     ISO Abbreviation:  Psychol Rev     Publication Date:  2009 Apr 
Date Detail:
Created Date:  2009-04-07     Completed Date:  2009-06-10     Revised Date:  2014-09-19    
Medline Journal Info:
Nlm Unique ID:  0376476     Medline TA:  Psychol Rev     Country:  United States    
Other Details:
Languages:  eng     Pagination:  439-53     Citation Subset:  IM    
Copyright Information:
(c) 2009 APA, all rights reserved
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MeSH Terms
Descriptor/Qualifier:
Attention
Bayes Theorem*
Behavioral Research / statistics & numerical data*
Conditioning, Classical
Humans
Learning
Likelihood Functions*
Psychomotor Performance
Grant Support
ID/Acronym/Agency:
R01 MH077027/MH/NIMH NIH HHS; R01 MH077027-02/MH/NIMH NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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