How to get statistically significant effects in any ERP experiment (and why you shouldn't).
How to get statistically significant effects in any ERP experiment (and why you shouldn't).
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DOI：
10.1111/psyp.12639
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发表时间：
2017-01
期刊：
影响因子：
3.7
通讯作者：
Gaspelin N
中科院分区：
文献类型：
--
作者：
Luck SJ;Gaspelin N
事件相关电位(ERP)实验产生了大量的数据集，通常包含每个参与者的数千个值，即使在平均后也是如此。这些数据集的丰富性在测试复杂的假设时可能非常有用，但这种丰富性也创造了许多机会，以获得具有统计意义但不能反映群体或条件之间的真实差异的效果(虚假效果)。这篇文章的目的是展示常见的和看似无害的量化和分析ERP效应的方法如何导致非常高的显著但虚假效果的比率，在许多实验中至少获得一个这样的虚假效果的可能性超过50%。我们专注于两个具体的问题：使用总平均数据来选择时间窗口和电极位置来量化分量幅度和潜伏期，以及使用一个或多个多因素统计分析。对先前数据的重新分析和对典型实验设计的模拟被用来说明这些问题如何极大地增加了显著但虚假结果的可能性。文中描述了几种策略来避免这些问题，并增加重大影响实际反映群体或条件之间真正差异的可能性。
Event-related potential (ERP) experiments generate massive data sets, often containing thousands of values for each participant, even after averaging. The richness of these data sets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common and seemingly innocuous methods for quantifying and analyzing ERP effects can lead to very high rates of significant-but-bogus effects, with the likelihood of obtaining at least one such bogus effect exceeding 50% in many experiments. We focus on two specific problems: using the grand average data to select the time windows and electrode sites for quantifying component amplitudes and latencies, and using one or more multi-factor statistical analyses. Re-analyses of prior data and simulations of typical experimental designs are used to show how these problems can greatly increase the likelihood of significant-but-bogus results. Several strategies are described for avoiding these problems and for increasing the likelihood that significant effects actually reflect true differences among groups or conditions.
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