Department of Psychological Methods, University of Amsterdam Amsterdam, Netherlands ; Medical Research Council - Cognition and Brain Sciences Unit Cambridge, UK.
Front Psychol. 2013 Aug 12;4:513. doi: 10.3389/fpsyg.2013.00513. eCollection 2013.
The direction of an association at the population-level may be reversed within the subgroups comprising that population-a striking observation called Simpson's paradox. When facing this pattern, psychologists often view it as anomalous. Here, we argue that Simpson's paradox is more common than conventionally thought, and typically results in incorrect interpretations-potentially with harmful consequences. We support this claim by reviewing results from cognitive neuroscience, behavior genetics, clinical psychology, personality psychology, educational psychology, intelligence research, and simulation studies. We show that Simpson's paradox is most likely to occur when inferences are drawn across different levels of explanation (e.g., from populations to subgroups, or subgroups to individuals). We propose a set of statistical markers indicative of the paradox, and offer psychometric solutions for dealing with the paradox when encountered-including a toolbox in R for detecting Simpson's paradox. We show that explicit modeling of situations in which the paradox might occur not only prevents incorrect interpretations of data, but also results in a deeper understanding of what data tell us about the world.
人群水平上的关联方向在构成该人群的亚组中可能会发生逆转——这一引人注目的观察结果被称为辛普森悖论。当面对这种模式时,心理学家通常认为它是异常的。在这里,我们认为辛普森悖论比人们通常认为的更为常见,并且通常会导致错误的解释——可能会产生有害的后果。我们通过回顾认知神经科学、行为遗传学、临床心理学、人格心理学、教育心理学、智力研究和模拟研究的结果来支持这一观点。我们表明,当从不同的解释水平(例如,从人群到亚组,或从亚组到个体)进行推断时,辛普森悖论最有可能发生。我们提出了一组表示悖论的统计标记,并提供了遇到悖论时处理悖论的心理测量解决方案——包括用于检测辛普森悖论的 R 中的工具箱。我们表明,对可能出现悖论的情况进行明确建模不仅可以防止对数据的错误解释,还可以加深我们对数据告诉我们关于世界的认识。