Loading…
“Magnitude-based Inference”: A Statistical Review
PURPOSEWe consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means. METHODSWe extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how “magnitude-based i...
Saved in:
Published in: | Medicine and science in sports and exercise 2015-04, Vol.47 (4), p.874-884 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c3931-b191cb7f3bd267a30f377caea42d6fadc3778a7d89b1904d8102d1be7a2b0c4f3 |
container_end_page | 884 |
container_issue | 4 |
container_start_page | 874 |
container_title | Medicine and science in sports and exercise |
container_volume | 47 |
creator | Welsh, Alan H Knight, Emma J |
description | PURPOSEWe consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means.
METHODSWe 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 CONCLUSIONSWe 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. |
doi_str_mv | 10.1249/MSS.0000000000000451 |
format | article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5642352</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1664776550</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3931-b191cb7f3bd267a30f377caea42d6fadc3778a7d89b1904d8102d1be7a2b0c4f3</originalsourceid><addsrcrecordid>eNp9kM9Kw0AQxhdRbK2-gUiOXlJ3stls4kEoxT8FRbB6Xja7kzaaJnU3afHWB9GX65MYqYp6cC7DML_vm-Ej5BBoH4IwObkZj_v0Z4UctkgXOKM-ZcC3SZdCwv0EGHTInnOPLSMYg13SCTjlwGLRJXy9er1RkzKvG4N-qhwab1RmaLHUuF69nXoDb1yrOnd1rlXh3eEix-U-2clU4fDgs_fIw8X5_fDKv769HA0H175mCQM_hQR0KjKWmiASitGMCaEVqjAwUaaMbsdYCRMnLUlDEwMNDKQoVJBSHWasR842vvMmnaHRWNZWFXJu85myL7JSufy9KfOpnFQLyaMwYDxoDY4_DWz13KCr5Sx3GotClVg1TkIUhUJEnNMWDTeotpVzFrPvM0DlR-KyTVz-TbyVHf188Vv0FXELxBtgWRU1WvdUNEu0coqqqKf_e78DvxGPGw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1664776550</pqid></control><display><type>article</type><title>“Magnitude-based Inference”: A Statistical Review</title><source>HEAL-Link subscriptions: Lippincott Williams & Wilkins</source><creator>Welsh, Alan H ; Knight, Emma J</creator><creatorcontrib>Welsh, Alan H ; Knight, Emma J</creatorcontrib><description>PURPOSEWe consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means.
METHODSWe 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 CONCLUSIONSWe 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.</description><identifier>ISSN: 0195-9131</identifier><identifier>EISSN: 1530-0315</identifier><identifier>DOI: 10.1249/MSS.0000000000000451</identifier><identifier>PMID: 25051387</identifier><language>eng</language><publisher>United States: American College of Sports Medicine</publisher><subject>Bayes Theorem ; Data Interpretation, Statistical ; Humans ; SPECIAL COMMUNICATIONS: Invited ; Sports Medicine - statistics & numerical data</subject><ispartof>Medicine and science in sports and exercise, 2015-04, Vol.47 (4), p.874-884</ispartof><rights>2015 American College of Sports Medicine</rights><rights>Copyright © 2014 by the American College of Sports Medicine 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3931-b191cb7f3bd267a30f377caea42d6fadc3778a7d89b1904d8102d1be7a2b0c4f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25051387$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Welsh, Alan H</creatorcontrib><creatorcontrib>Knight, Emma J</creatorcontrib><title>“Magnitude-based Inference”: A Statistical Review</title><title>Medicine and science in sports and exercise</title><addtitle>Med Sci Sports Exerc</addtitle><description>PURPOSEWe consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means.
METHODSWe 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 CONCLUSIONSWe 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.</description><subject>Bayes Theorem</subject><subject>Data Interpretation, Statistical</subject><subject>Humans</subject><subject>SPECIAL COMMUNICATIONS: Invited</subject><subject>Sports Medicine - statistics & numerical data</subject><issn>0195-9131</issn><issn>1530-0315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kM9Kw0AQxhdRbK2-gUiOXlJ3stls4kEoxT8FRbB6Xja7kzaaJnU3afHWB9GX65MYqYp6cC7DML_vm-Ej5BBoH4IwObkZj_v0Z4UctkgXOKM-ZcC3SZdCwv0EGHTInnOPLSMYg13SCTjlwGLRJXy9er1RkzKvG4N-qhwab1RmaLHUuF69nXoDb1yrOnd1rlXh3eEix-U-2clU4fDgs_fIw8X5_fDKv769HA0H175mCQM_hQR0KjKWmiASitGMCaEVqjAwUaaMbsdYCRMnLUlDEwMNDKQoVJBSHWasR842vvMmnaHRWNZWFXJu85myL7JSufy9KfOpnFQLyaMwYDxoDY4_DWz13KCr5Sx3GotClVg1TkIUhUJEnNMWDTeotpVzFrPvM0DlR-KyTVz-TbyVHf188Vv0FXELxBtgWRU1WvdUNEu0coqqqKf_e78DvxGPGw</recordid><startdate>201504</startdate><enddate>201504</enddate><creator>Welsh, Alan H</creator><creator>Knight, Emma J</creator><general>American College of Sports Medicine</general><general>Lippincott Williams & Wilkins</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201504</creationdate><title>“Magnitude-based Inference”: A Statistical Review</title><author>Welsh, Alan H ; Knight, Emma J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3931-b191cb7f3bd267a30f377caea42d6fadc3778a7d89b1904d8102d1be7a2b0c4f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Bayes Theorem</topic><topic>Data Interpretation, Statistical</topic><topic>Humans</topic><topic>SPECIAL COMMUNICATIONS: Invited</topic><topic>Sports Medicine - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Welsh, Alan H</creatorcontrib><creatorcontrib>Knight, Emma J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medicine and science in sports and exercise</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Welsh, Alan H</au><au>Knight, Emma J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>“Magnitude-based Inference”: A Statistical Review</atitle><jtitle>Medicine and science in sports and exercise</jtitle><addtitle>Med Sci Sports Exerc</addtitle><date>2015-04</date><risdate>2015</risdate><volume>47</volume><issue>4</issue><spage>874</spage><epage>884</epage><pages>874-884</pages><issn>0195-9131</issn><eissn>1530-0315</eissn><abstract>PURPOSEWe consider “magnitude-based inference” and its interpretation by examining in detail its use in the problem of comparing two means.
METHODSWe 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 CONCLUSIONSWe 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.</abstract><cop>United States</cop><pub>American College of Sports Medicine</pub><pmid>25051387</pmid><doi>10.1249/MSS.0000000000000451</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0195-9131 |
ispartof | Medicine and science in sports and exercise, 2015-04, Vol.47 (4), p.874-884 |
issn | 0195-9131 1530-0315 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5642352 |
source | HEAL-Link subscriptions: Lippincott Williams & Wilkins |
subjects | Bayes Theorem Data Interpretation, Statistical Humans SPECIAL COMMUNICATIONS: Invited Sports Medicine - statistics & numerical data |
title | “Magnitude-based Inference”: A Statistical Review |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T01%3A07%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=%E2%80%9CMagnitude-based%20Inference%E2%80%9D:%20A%20Statistical%20Review&rft.jtitle=Medicine%20and%20science%20in%20sports%20and%20exercise&rft.au=Welsh,%20Alan%20H&rft.date=2015-04&rft.volume=47&rft.issue=4&rft.spage=874&rft.epage=884&rft.pages=874-884&rft.issn=0195-9131&rft.eissn=1530-0315&rft_id=info:doi/10.1249/MSS.0000000000000451&rft_dat=%3Cproquest_pubme%3E1664776550%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3931-b191cb7f3bd267a30f377caea42d6fadc3778a7d89b1904d8102d1be7a2b0c4f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1664776550&rft_id=info:pmid/25051387&rfr_iscdi=true |