Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels.
This article argues that researchers do not need to completely abandon the p-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a sign...
| Publicado en: | American Statistician Vol. 73; pp. 213 - 223 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Mar2019 Supplement
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136176755&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136176755 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Mar2019 Supplement vid: 73 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 136176755 10.1080/00031305.2018.1518268 ppf: 213 ppct: 10 formats: tig: atl: Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels. aug: au: Gannon, Mark Andrew de Bragança Pereira, Carlos Alberto Polpo, Adriano affil: Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil Instituto de Matemática Aplicada, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil Department of Statistics, Federal University of São Carlos su: Bayesian analysis Statistical significance Hardy-Weinberg formula Neyman-Pearson theorem Sample size (Statistics) Likelihood ratio tests Null hypothesis sug: subj: Bayesian analysis Statistical significance Hardy-Weinberg formula Neyman-Pearson theorem Sample size (Statistics) Likelihood ratio tests Null hypothesis keyword: Hardy–Weinberg equilibrium Neyman–Pearson lemma Predictive distribution Significance test Hardy–Weinberg equilibrium Neyman–Pearson lemma Predictive distribution Significance test ab: This article argues that researchers do not need to completely abandon the p-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of α, the probability of rejecting a true null hypothesis, and β, the probability of failing to reject a false null, instead of fixing α and minimizing β. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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