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零假设显著性检验:简短教程。

Null hypothesis significance testing: a short tutorial.

作者信息

Pernet Cyril

机构信息

Centre for Clinical Brain Sciences (CCBS), Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK.

出版信息

F1000Res. 2015 Aug 25;4:621. doi: 10.12688/f1000research.6963.3. eCollection 2015.

Abstract

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 tutorial, 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 reporting practices.

摘要

尽管受到了彻底的批评,但零假设显著性检验(NHST)仍然是生物学、生物医学和社会科学中用于提供效应证据的首选统计方法。在本简短教程中,我首先总结该方法背后的概念,区分显著性检验(费舍尔)和接受性检验(纽曼 - 皮尔逊),并指出关于p值的常见解释错误。然后我介绍置信区间的相关概念,并再次指出常见的解释错误。最后,我讨论在何种情况下应该报告哪些内容。目标是澄清概念以避免解释错误,并提出报告规范。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebf5/5635622/fe13f0edc593/f1000research-4-10487-g0000.jpg

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