It is important to be able to identify research results likely to have been arrived at by means of “p-hacking,” a common term for research and reporting practices (such as the selective reporting of results) that are biased toward finding p < α. This paper discusses and demonstrates “p-curving,” a means of checking a set of primary studies within a specific research stream for signs of p-hacking. A salient feature of p-curving is that it is based entirely on significant p-values. Because of the potential usefulness of p-curving and because it has been little used by SLA researchers, a case study illustrates the construction and analysis of a p-curve as a complement to meta-analysis. The focal p-curve in this study relates to published (quasi)experimental studies that addressed the research hypothesis that for low and middle proficiency learners L1 glosses facilitate vocabulary learning during reading better than L2 glosses do.

重要的是能够识别可能通过“ p -hacking”获得的研究结果，这是一个常用术语，用于偏向于发现p < α的研究和报告实践（例如结果的选择性报告） . 本文讨论并演示了“ p -curving”，这是一种检查特定研究流中的一组主要研究是否存在 p-hacking 迹象的方法。p曲线的一个显着特征是它完全基于显着的p值。由于p曲线的潜在用途以及 SLA 研究人员很少使用它，一个案例研究说明了 p 曲线的构建和分析-曲线作为荟萃分析的补充。本研究中的焦点p曲线与已发表的（准）实验研究相关，这些研究解决了以下研究假设：对于中低水平的学习者，L1 语言比 L2 语言更能促进阅读过程中的词汇学习。