The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data

Gilles E. Gignac and Marcin Zajenkowski

Intelligence, 2020, vol. 80, issue C

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.

Keywords: Dunning-Kruger effect; Intelligence; Self-assessed intelligence (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:80:y:2020:i:c:s0160289620300271

DOI: 10.1016/j.intell.2020.101449

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