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Nonsignificance Plus High Power Does Not Imply Support for the Null Over the Alternative

机译:无意义加高功率并不意味着支持替代方案为零

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摘要

This article summarizes arguments against the use of power to analyze data, and illustrates a key pitfall: Lack of statistical significance (e.g., p > .05) combined with high power (e.g., 90%) can occur even if the data support the alternative more than the null. This problem arises via selective choice of parameters at which power is calculated, but can also arise if one computes power at a prespecified alternative. As noted by earlier authors, power computed using sample estimates (" observed power" ) replaces this problem with even more counterintuitive behavior, because observed power effectively double counts the data and increases as the P value declines. Use of power to analyze and interpret data thus needs more extensive discouragement.
机译:本文总结了反对使用功率来分析数据的论点,并说明了一个关键陷阱:即使数据支持替代方法,也可能发生缺乏统计显着性(例如p> .05)和高功率(例如90%)的情况。大于null。通过选择计算功率的参数会出现此问题,但如果以预定的替代方法计算功率,也会出现此问题。如先前的作者所指出的,使用样本估计值计算的功效(“观察的功效”)用更违反直觉的行为代替了此问题,因为观察的功效有效地使数据加倍计数,并随着P值的降低而增加。因此,使用权力来分析和解释数据需要更大的劝阻。

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