Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/110054
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Type: Journal article
Title: "Magnitude-based inference": a statistical review
Author: Welsh, A.H.
Knight, E.J.
Citation: Medicine and Science in Sports and Exercise, 2015; 47(4):874-884
Publisher: American College of Sports Medicine
Issue Date: 2015
ISSN: 0195-9131
1530-0315
Statement of
Responsibility: 
Alan H. Welsh, Emma J. Knight
Abstract: 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.
Keywords: Bayesian; Behrens-Fisher; confidence interval; frequentist
Rights: © 2014 by the American College of Sports Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.
DOI: 10.1249/mss.0000000000000451
Published version: http://dx.doi.org/10.1249/mss.0000000000000451
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