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“Magnitude-based Inference”: A Statistical Review

PURPOSE: We consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means. METHODS: We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how “magnitude-bas...

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Detalles Bibliográficos
Autores principales: Welsh, Alan H., Knight, Emma J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642352/
https://www.ncbi.nlm.nih.gov/pubmed/25051387
http://dx.doi.org/10.1249/MSS.0000000000000451
Descripción
Sumario:PURPOSE: We consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means. METHODS: We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how “magnitude-based inference” is implemented. We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms. RESULTS AND CONCLUSIONS: We show that “magnitude-based inference” is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with “magnitude-based inference” and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using “magnitude-based inference,” a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis.