The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data
The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data
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DOI：
10.1080/00031305.2017.1322143
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发表时间：
2019-01-02
影响因子：
1.8
通讯作者：
Pham, Philip
中科院分区：
文献类型：
--
作者：
Mutz, Diana C.;Pemantle, Robin;Pham, Philip
对公布结果的可信度的广泛关注导致了对统计做法的审查。我们解决这个问题的一个方面，源于使用平衡测试结合实验数据。当随机分配失败时，无论是由于实施中的错误还是由于差异性损耗，平衡测试都可以成为决定是否将数据视为观察数据还是实验数据的重要工具。不幸的是，在分析“干净”数据（即可以规定随机分配的数据）时，平衡检验的使用已变得司空见惯。在这里，我们表明，平衡测试可以破坏科学结论形成的基础，并可能导致错误甚至欺诈性的结论。最后，我们提倡科学家和期刊编辑在所有干净数据的分析中抵制使用平衡检验。这篇文章的补充材料可以在网上找到
Widespread concern over the credibility of published results has led to scrutiny of statistical practices. We address one aspect of this problem that stems from the use of balance tests in conjunction with experimental data. When random assignment is botched, due either to mistakes in implementation or differential attrition, balance tests can be an important tool in determining whether to treat the data as observational versus experimental. Unfortunately, the use of balance tests has become commonplace in analyses of "clean" data, that is, data for which random assignment can be stipulated. Here, we show that balance tests can destroy the basis on which scientific conclusions are formed, and can lead to erroneous and even fraudulent conclusions. We conclude by advocating that scientists and journal editors resist the use of balance tests in all analyses of clean data. Supplementary materials for this article are available online