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The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data
Intelligence ( IF 3.613 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.intell.2020.101449
Gilles E. Gignac , Marcin Zajenkowski

Abstract The Dunning-Kruger hypothesis states that the degree to which people can estimate their ability accurately depends, in part, upon possessing the ability in question. Consequently, people with lower levels of the ability tend to self-assess their ability less well than people who have relatively higher levels of the ability. The most common method used to test the Dunning-Kruger hypothesis involves plotting the self-assessed and objectively assessed means across four categories (quartiles) of objective ability. However, this method has been argued to be confounded by the better-than-average effect and regression toward the mean. In this investigation, it is argued that the Dunning-Kruger hypothesis can be tested validly with two inferential statistical techniques: the Glejser test of heteroscedasticity and nonlinear (quadratic) regression. On the basis of a sample of 929 general community participants who completed a self-assessment of intelligence and the Advanced Raven's Progressive Matrices, we failed to identify statistically significant heteroscedasticity, contrary to the Dunning-Kruger hypothesis. Additionally, the association between objectively measured intelligence and self-assessed intelligence was found to be essentially entirely linear, again, contrary to the Dunning-Kruger hypothesis. It is concluded that, although the phenomenon described by the Dunning-Kruger hypothesis may be to some degree plausible for some skills, the magnitude of the effect may be much smaller than reported previously.

中文翻译:

Dunning-Kruger 效应(主要)是一种统计人工制品:使用个体差异数据检验假设的有效方法

摘要 Dunning-Kruger 假说指出,人们能够准确估计自己能力的程度部分取决于拥有相关能力。因此,与能力水平相对较高的人相比,能力水平较低的人往往对自己的能力进行自我评估。用于检验 Dunning-Kruger 假设的最常用方法涉及绘制跨越四个客观能力类别(四分位数)的自我评估和客观评估均值。然而,这种方法被认为会被优于平均水平的效果和回归均值所混淆。在这项调查中,有人认为可以使用两种推理统计技术有效地检验 Dunning-Kruger 假设:异方差性和非线性(二次)回归的 Glejser 检验。根据完成智力自我评估和 Advanced Raven 渐进矩阵的 929 名普通社区参与者的样本,我们未能确定具有统计意义的异方差性,这与 Dunning-Kruger 假设相反。此外,发现客观测量智力和自我评估智力之间的关联基本上完全是线性的,这与邓宁-克鲁格假设相反。得出的结论是,尽管 Dunning-Kruger 假设描述的现象对于某些技能可能在某种程度上是合理的,但其影响的幅度可能比之前报告的要小得多。与 Dunning-Kruger 假设相反,我们未能确定统计上显着的异方差性。此外,发现客观测量智力和自我评估智力之间的关联基本上完全是线性的,这与邓宁-克鲁格假设相反。得出的结论是,尽管 Dunning-Kruger 假设描述的现象对于某些技能可能在某种程度上是合理的,但其影响的幅度可能比之前报告的要小得多。与 Dunning-Kruger 假设相反,我们未能确定统计上显着的异方差性。此外,发现客观测量智力和自我评估智力之间的关联基本上完全是线性的,这与邓宁-克鲁格假设相反。得出的结论是,尽管 Dunning-Kruger 假设描述的现象对于某些技能可能在某种程度上是合理的,但其影响的幅度可能比之前报告的要小得多。
更新日期:2020-05-01
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