在回归模型中测试正常性:错误非常多 (但可能并不重要)
Stephen Midway1, J Wilson White2
1Department of Oceanography & Coastal Sciences, Louisiana State University, Baton Rouge, LA, USA.
Royal Society open science
|May 1, 2025
概括
生态学家和生物学家经常滥用原始数据的正常性测试,而不是模型残留. 正确测试残留物提高了统计能力,尽管测试原始数据对功率的影响最小.
科学领域:
- 生态生态学 生态生态学
- 生物学 生物学 生物学
- 统计 统计 统计 统计
背景情况:
- 正常性测试对于评估统计建模中的假设至关重要.
- 关于正常性测试的应用的误解在生态和生物研究中很普遍.
- 超过70%的生态学和90%的生物学论文错误地测试原始数据的正常性,而不是模型残留.
研究的目的:
- 调查误用正常性测试对原始数据与线性回归中的模型残余的影响.
- 在不同的正常性测试方法下,比较参数 (t-test) 和非参数 (Mann-Whitney U测试) 方法的统计能力.
- 评估生态和生物研究中常见的统计错误的后果.
主要方法:
- 对已发表的生态和生物学论文进行文献计量审查,以确定正常性测试应用的趋势.
- 模拟具有不同分布 (正常,间隔,斜) 和样本大小的数据集.
- 测试原始数据和测试模型残留物之间的统计能力的比较.
主要成果:
- 在与残留数据相比,在原始数据上测试正常性时,观察到统计能力的最小差异.
- 当模型余量违反正常性假设时,曼-惠特尼U测试显示,与不正确方法相比,统计能力增加了3-4%.
- 测试原始数据的正常性对统计能力的影响是可以忽略不计的,特别是在大样本大小的情况下.
结论:
- 建议正确测试模型残留的正常性,以提高统计模型的性能.
- 虽然需要正确的统计实践意识,但测试原始数据对功耗损失正常性的实际影响是最小的.
- 该研究强调了坚持统计假设对于强大的生态和生物研究的重要性.
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