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Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty

机译:更准确的统计功率的示例大小规划:一种调整样本效果大小的方法,用于出版物偏差和不确定性

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摘要

The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.
机译:获得所需级别统计功率所需的样本量依赖于效果大小的人口值,即根据定义未知。样本尺寸计划的常见方法使用先前研究的样本效果大小作为在未来研究中待检测到的效果的人口价值的估计。尽管该策略直观地吸引,但由于出版物偏见和不确定性,截至面值的效应估计通常不准确估计人口效应规模。我们表明,这种方法的使用往往会导致高动力的研究,有时会出现惊人程度。我们提出了一种调整偏差和不确定性的样本效果大小的替代方法,我们展示了几种实验设计的有效性。此外,我们讨论了我们为研究人员提供的开源R包,BUCS和用户友好的Web应用程序,以便他们可以轻松实现我们建议的方法。

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