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首页> 外文期刊>Proceedings of the Royal Society. Biological sciences >Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data
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Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data

机译：统计模型规范和权力：关于在实验数据分析中使用测试合格池的建议

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摘要

A common approach to the analysis of experimental data across much of the biological sciences is test-qualified pooling. Here non-significant terms are dropped from a statistical model, effectively pooling the variation associated with each removed term with the error term used to test hypotheses (or estimate effect sizes). This pooling is only carried out if statistical testing on the basis of applying that data to a previous more complicated model provides motivation for this model simplification; hence the pooling is test-qualified. In pooling, the researcher increases the degrees of freedom of the error term with the aim of increasing statistical power to test their hypotheses of interest. Despite this approach being widely adopted and explicitly recommended by some of the most widely cited statistical textbooks aimed at biologists, here we argue that (except in highly specialized circumstances that we can identify) the hoped-for improvement in statistical power will be small or non-existent, and there is likely to be much reduced reliability of the statistical procedures through deviation of type I error rates from nominal levels. We thus call for greatly reduced use of test-qualified pooling across experimental biology, more careful justification of any use that continues, and a different philosophy for initial selection of statistical models in the light of this change in procedure.
机译：跨大部分生物科学分析实验数据的常见方法是测试合格的汇集。这里，非重大术语从统计模型中删除，有效地汇集了与每个移除术语相关的变化，其中误差术语用于测试假设（或估计效果大小）。只有在将数据应用于先前的更复杂模型的基础上的统计测试提供了此汇集，则仅执行统计测试，为此模型的简化提供了动机;因此，汇集是测试合格的。在汇集方面，研究人员增加了误差项的自由度，目的是增加统计能力来测试他们的假设的假设。尽管这种方法被广泛采用和明确地建议，其中一些被瞄准的生物学家的一些最广泛引用的统计教科书，在这里，我们认为（除了我们可以识别的高度专业情况之外，我们可以识别的高度专业情况）希望改善统计能力将是小的或非 - 通过从名义级别的I型错误率偏差，可能会降低统计过程的可靠性。因此，我们要求大大减少使用测试合格的汇集在实验生物学中的使用，更仔细地对任何持续的使用的理由，以及根据该过程的这种变化，初始选择统计模型的不同哲学。

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