统计假设检验:医学研究的一般方法

A. Suvorov, N. Bulanov, A. N. Shvedova, E. A. Tao, D. Butnaru, M. Nadinskaia, A. Zaikin
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引用次数: 1

摘要

统计假设检验是现代医学研究的关键步骤之一。最初,科学家制定一个研究假设,然后在此基础上发展统计假设并进行统计检验。本文给出了针对不同研究问题的原假设和备假设的编制示例,以及用t检验检验它们的一般算法。作者还描述了I型误差,这是解释从统计检验中估计的p值所必需的,以及II型误差,这是用来评估研究能力的。本文重点介绍了效应量及其计算方法,以及统计显著效应与临床显著效应的区别。还讨论了效应量、样本量和II型误差之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical hypothesis testing: general approach in medical research
Statistical hypothesis testing is one of the key steps in modern medical research. Initially, scientists formulate a research hypothesis based on which the statistical hypothesis is then developed and statistically tested. This review provides the null and alternative hypotheses’ compiling examples for different research questions and the general algorithm for their testing using t-test. The authors also describe type I errors, which are necessary to interpret p-values estimated from statistical tests, and type II errors, which are used to assess study power. The article focuses on effect size and its calculation methods, and the difference between statistically significant and clinically significant effects. The associations between effect size, sample size, and type II error are also discussed.
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