Researchers are traditionally advised to plan for their required sample size such that achieving a sufficient level of statistical power is ensured (Cohen, 1988). While this method helps distinguishing statistically significant effects from the nonsignificant ones, it does not help achieving the higher goal of accurately estimating the actual size of those effects in an intended study. Adopting an open-science approach, this article presents an alternative approach, accuracy in effect size estimation (AESE), to sample size planning that ensures that researchers obtain adequately narrow confidence intervals (CI) for their effect sizes of interest thereby ensuring accuracy in estimating the actual size of those effects. Specifically, I (a) compare the underpinnings of power-analytic and AESE methods, (b) provide a practical definition of narrow CIs, (c) apply the AESE method to various research studies from L2 literature, and (d) offer several flexible R programs to implement the methods discussed in this article.

中文翻译：

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定量 L2 研究中的样本规模规划

传统上建议研究人员计划他们所需的样本量，以确保达到足够的统计能力水平（Cohen，1988）。虽然这种方法有助于区分统计学上显着的影响和不显着的影响，但它无助于实现在预期研究中准确估计这些影响的实际大小的更高目标。本文采用开放科学的方法，提出了另一种方法，效应量估计的准确性(AESE)，以确保研究人员对其感兴趣的效应大小获得足够窄的置信区间 (CI)，从而确保准确估计这些效应的实际大小。具体来说，我 (a) 比较了功率分析和 AESE 方法的基础，(b) 提供了狭义 CI 的实用定义，(c) 将 AESE 方法应用于 L2 文献中的各种研究，以及 (d) 提供了几种灵活的用于实现本文中讨论的方法的 R 程序。