样本量测定
样品(材料)
统计
统计能力
人口
人口规模
价值(数学)
计算机科学
计量经济学
数学
色谱法
社会学
人口学
化学
作者
Samantha F. Anderson,Ken Kelley,Scott E. Maxwell
标识
DOI:10.1177/0956797617723724
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI