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弥天大谎:-黑客攻击策略汇编与模拟

Big little lies: a compendium and simulation of -hacking strategies.

作者信息

Stefan Angelika M, Schönbrodt Felix D

机构信息

Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Department of Psychology, Universität der Bundeswehr München, München, Germany.

出版信息

R Soc Open Sci. 2023 Feb 8;10(2):220346. doi: 10.1098/rsos.220346. eCollection 2023 Feb.

Abstract

In many research fields, the widespread use of questionable research practices has jeopardized the credibility of scientific results. One of the most prominent questionable research practices is -hacking. Typically, -hacking is defined as a compound of strategies targeted at rendering non-significant hypothesis testing results significant. However, a comprehensive overview of these -hacking strategies is missing, and current meta-scientific research often ignores the heterogeneity of strategies. Here, we compile a list of 12 -hacking strategies based on an extensive literature review, identify factors that control their level of severity, and demonstrate their impact on false-positive rates using simulation studies. We also use our simulation results to evaluate several approaches that have been proposed to mitigate the influence of questionable research practices. Our results show that investigating -hacking at the level of strategies can provide a better understanding of the process of -hacking, as well as a broader basis for developing effective countermeasures. By making our analyses available through a Shiny app and R package, we facilitate future meta-scientific research aimed at investigating the ramifications of -hacking across multiple strategies, and we hope to start a broader discussion about different manifestations of -hacking in practice.

摘要

在许多研究领域,有问题的研究行为的广泛使用损害了科学结果的可信度。最突出的有问题的研究行为之一是p值操纵。通常,p值操纵被定义为一系列旨在使无统计学意义的假设检验结果变得具有统计学意义的策略的组合。然而,目前缺少对这些p值操纵策略的全面概述,并且当前的元科学研究常常忽略策略的异质性。在此,我们基于广泛的文献综述编制了一份包含12种p值操纵策略的清单,确定了控制其严重程度的因素,并通过模拟研究展示了它们对假阳性率的影响。我们还利用模拟结果评估了为减轻有问题的研究行为的影响而提出的几种方法。我们的结果表明,在策略层面研究p值操纵能够更好地理解p值操纵过程,也能为制定有效的应对措施提供更广泛的基础。通过一个Shiny应用程序和R包提供我们的分析结果,我们推动了未来旨在研究多种策略下p值操纵影响的元科学研究,并且我们希望开启一场关于p值操纵在实践中的不同表现形式的更广泛讨论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/376b/9905987/f859b83c0cc8/rsos220346f01.jpg

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