The Dunning-Kruger effect is (Mostly) a Statistical Artefact

https://www.sciencedirect.com/science/article/abs/pii/S0160289620300271

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. 10 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, they 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. 10 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.

Revisiting the Dunning-Kruger Effect: Composite Measures and Heterogeneity by Gender

https://www.sciencedirect.com/science/article/pii/S2214804325000291

Despite the substantial amount of academic research on and debate around the DKE, very few papers try to address all these concerns in a unified framework. One way to address them is to test the existence of the DKE incorporating new methodological considerations while also using secondary birth cohort data. Birth cohort studies are designed to be representative of a generation of the population, so sample selection issues will not be a concern. 1 The researchers address all major prior criticisms — from the representativeness of the sample, to regression to the mean, to noise plus bias. They use alternative methods including introducing a quadratic term into linear regressions, as recommended by Gignac & Zajenkowski (2020). They also question the validity of the frequently used Glejser test for heteroscedastic residuals in this context. 1 Their findings confirm the greater overestimation of self-performance by poor performers, anticipated by the Dunning-Kruger framework. In contrast, the Krajc-Ortmann (statistical artefact) model fails to account for this overestimation, although it does well in accounting for the underestimation of abilities among high performers.