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当零假设显著性检验不适用于研究时:重新评估

When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment.

作者信息

Szucs Denes, Ioannidis John P A

机构信息

Department of Psychology, University of CambridgeCambridge, United Kingdom.

Meta-Research Innovation Center at Stanford and Department of Medicine, Department of Health Research and Policy, and Department of Statistics, Stanford UniversityStanford, CA, United States.

出版信息

Front Hum Neurosci. 2017 Aug 3;11:390. doi: 10.3389/fnhum.2017.00390. eCollection 2017.

Abstract

Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out.

摘要

零假设显著性检验(NHST)存在若干缺陷,这些缺陷可能是导致(认知)神经科学、心理学及一般生物医学领域广泛存在的、备受争议的重复危机的潜在因素。我们回顾了这些缺陷,并认为,在经历了持续的负面经验后,NHST不应再成为所有生物医学和心理学研究默认的、主导的统计方法。如果理论预测不够有力,我们不应依赖非此即彼的假设检验。不同的推理方法可能最适用于不同类型的研究问题。每当研究人员使用NHST时,他们都应说明使用理由,并公布研究前的功效计算和效应量,包括阴性结果。假设检验研究应进行预注册,并最好公布原始数据。目前那种维持了NHST广泛、虚假使用的针对学生的简化统计教育方法应逐步淘汰。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/5540883/94978edb823e/fnhum-11-00390-g0001.jpg

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