Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]

Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of si...

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Bibliographic Details
Main Author: Cyril Pernet
Format: Article
Language:English
Published: F1000 Research Ltd 2017-10-01
Series:F1000Research
Subjects:
Statistical Methodologies & Health Informatics
Online Access:https://f1000research.com/articles/4-621/v5
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spelling doaj-7c664598dac54217adc0c7f2c04514332020-11-25T02:49:51ZengF1000 Research LtdF1000Research2046-14022017-10-01410.12688/f1000research.6963.513986Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]Cyril Pernet0Centre for Clinical Brain Sciences (CCBS), Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UKAlthough thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.https://f1000research.com/articles/4-621/v5Statistical Methodologies & Health Informatics
collection DOAJ
language English
format Article
sources DOAJ
author Cyril Pernet
spellingShingle Cyril Pernet
Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
F1000Research
Statistical Methodologies & Health Informatics
author_facet Cyril Pernet
author_sort Cyril Pernet
title Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
title_short Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
title_full Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
title_fullStr Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
title_full_unstemmed Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
title_sort null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice [version 5; referees: 2 approved, 2 not approved]
publisher F1000 Research Ltd
series F1000Research
issn 2046-1402
publishDate 2017-10-01
description Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.
topic Statistical Methodologies & Health Informatics
url https://f1000research.com/articles/4-621/v5
work_keys_str_mv AT cyrilpernet nullhypothesissignificancetestingaguidetocommonlymisunderstoodconceptsandrecommendationsforgoodpracticeversion5referees2approved2notapproved
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