为多个假设调整p值:为什么,何时以及如何
1Regional Centre for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Norway stian.lydersen@ntnu.no.
Annals of the rheumatic diseases
|May 9, 2024
概括
调查多个假设增加了I型错误的风险. 这篇文章详细介绍了多重度调整方法来控制这些统计错误,并为研究人员提供了建议.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 研究方法研究方法研究方法学
背景情况:
- 单个研究通常涉及多个假设,例如检查各种结果,时间点或子组.
- 这种做法提高了遇到假阳性结果的可能性,称为I型错误.
研究的目的:
- 描述统计分析中多重度调整的常用方法.
- 为控制多假设研究中的I型错误率提供建议.
主要方法:
- 这篇文章回顾了针对多重度调整而设计的既定统计程序.
- 讨论了控制I型错误概率的关键方法.
主要成果:
- 存在多种统计方法来管理在测试多个假设时出现I型错误的风险增加.
- 这篇文章概述了这些技术,并提供了关于其应用的指导.
结论:
- 适当的多重性调整对于保持研究结果的完整性至关重要.
- 实施推的调整方法有助于确保复杂研究中可靠的统计结论.
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