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      “Magnitude-based Inference”: A Statistical Review

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          Abstract

          Supplemental digital content is available in the text.

          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.

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          Most cited references30

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          Progressive statistics for studies in sports medicine and exercise science.

          Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
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            Statistical methods in psychology journals: Guidelines and explanations.

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              Making meaningful inferences about magnitudes.

              A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an inference about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or nonsignificant on the basis of a P value derived from a null-hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. The authors use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First they express the uncertainty as confidence limits, which define the likely range of the true value. They then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some positive and negative sense, such as beneficial or harmful. If the likely range overlaps substantially positive and negative values, they infer that the outcome is unclear; otherwise, they infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. They refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (eg, very likely beneficial). Quantitative or qualitative probabilities that the true value has the other 2 magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome.
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                Author and article information

                Journal
                Med Sci Sports Exerc
                Med Sci Sports Exerc
                MSS
                Medicine and Science in Sports and Exercise
                Lippincott Williams & Wilkins
                0195-9131
                1530-0315
                April 2015
                17 March 2015
                : 47
                : 4
                : 874-884
                Affiliations
                [1] 1Mathematical Sciences Institute, Australian National University, Canberra, Australian Capital Territory, AUSTRALIA; and 2Performance Research, Australian Institute of Sport, Belconnen, Australian Capital Territory, AUSTRALIA
                Author notes
                Address for correspondence: Emma Knight, Ph.D., Performance Research, Australian Institute of Sport, PO Box 176, Belconnen, Australian Capital Territory 2616, Australia; E-mail: emma.knight@ 123456ausport.gov.au .
                Article
                MSS14262 00027
                10.1249/MSS.0000000000000451
                5642352
                25051387
                19650c4f-8bdb-4105-8879-bbdea2ccd0fa
                Copyright © 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.

                History
                : March 2014
                : July 2014
                Categories
                SPECIAL COMMUNICATIONS: Invited Commentary
                Custom metadata
                TRUE
                T

                bayesian,behrens–fisher,confidence interval,frequentist

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