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Conditions of the Central-Limit Theorem Are Rarely Satisfied in Empirical Psychological Studies.

作者信息

Sawada Tadamasa

机构信息

School of Psychology, National Research University Higher School of Economics, Moscow, Russia.

出版信息

Front Psychol. 2021 Nov 8;12:762418. doi: 10.3389/fpsyg.2021.762418. eCollection 2021.

DOI:10.3389/fpsyg.2021.762418
PMID:34858289
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8630578/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f9/8630578/b1859c2d0aae/fpsyg-12-762418-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f9/8630578/b1859c2d0aae/fpsyg-12-762418-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f9/8630578/b1859c2d0aae/fpsyg-12-762418-g0001.jpg

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本文引用的文献

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More about the basic assumptions of t-test: normality and sample size.更详细地探讨 t 检验的基本假设:正态性和样本量。
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2
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Korean J Anesthesiol. 2017 Apr;70(2):144-156. doi: 10.4097/kjae.2017.70.2.144. Epub 2017 Feb 21.
3
Equivalent statistics and data interpretation.等效统计与数据解读。
心理学中关于置信区间的一些误解。
Front Psychol. 2022 Jul 22;13:948423. doi: 10.3389/fpsyg.2022.948423. eCollection 2022.
Behav Res Methods. 2017 Aug;49(4):1524-1538. doi: 10.3758/s13428-016-0812-3.
4
Revised standards for statistical evidence.修订后的统计证据标准。
Proc Natl Acad Sci U S A. 2013 Nov 26;110(48):19313-7. doi: 10.1073/pnas.1313476110. Epub 2013 Nov 11.
5
t-tests, non-parametric tests, and large studies--a paradox of statistical practice?t 检验、非参数检验与大型研究——统计实践中的悖论?
BMC Med Res Methodol. 2012 Jun 14;12:78. doi: 10.1186/1471-2288-12-78.
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The importance of the normality assumption in large public health data sets.正态性假设在大型公共卫生数据集中的重要性。
Annu Rev Public Health. 2002;23:151-69. doi: 10.1146/annurev.publhealth.23.100901.140546. Epub 2001 Oct 25.