Rand R. Wilcox, Guillaume A. Rousselet
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引用次数: 85
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Abstract
There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced understanding of data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, the vast array of new and improved techniques for comparing groups and studying associations can seem daunting, simply because there are so many new methods that are now available. This unit briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest some general guidelines regarding when, how, and why certain modern techniques might be used. © 2018 by John Wiley & Sons, Inc.
神经科学稳健统计方法指南
有大量新的和改进的方法用于比较群体和研究关联,这些方法有可能大幅提高能力,改善对第一类错误概率的控制,并对数据产生更深入、更细致的理解。这些新技术有效地解决了四个问题,即传统方法何时以及为什么不能令人满意。但对于非统计学家来说,用于比较群体和研究关联的大量新的和改进的技术似乎令人生畏,原因很简单,因为现在有太多的新方法可用。本单元简要回顾了传统方法何时以及为什么具有相对较低的功率并产生误导性结果。主要目标是提出一些关于何时、如何以及为什么可以使用某些现代技术的一般指导方针。©2018 by John Wiley &儿子,Inc。
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