publishing auto-correlation is not a 'data artefact', but a mistake.

and a rather ironic one at that.

The #Dunning-Kruger Effect is #Autocorrelation https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...

another modelling problem with it: ceiling effect https://news.ycombinator.com/item?id=38416412 How much can your top performers overestimate their performance? The opposite problem happens for the worst performers.

A Statistical Explanation of the Dunning–Kruger Effect https://www.frontiersin.org/journals/psychology/articles/10....

The DK effect says roughly, "low performers tend to overestimate their abilities." Yet when researchers analyzed the data, they found that high and low performers overestimate and underestimate with the same frequency. [0] It's just that high performers are more accurate than low performers (note how this statement differs from the DK effect). Since you can completely explain the "X graph" by the random noise combined with the ceiling effect, and since beginners' self evaluations are noisier than experts', you don't even need regression to the mean to explain why you get the "X graph."

0. Nuhfer, Edward, Steven Fleisher, Christopher Cogan, Karl Wirth, and Eric Gaze. "How Random Noise and a Graphical Convention Subverted Behavioral Scientists' Explanations of Self-Assessment Data: Numeracy Underlies Better Alternatives." Numeracy 10, Iss. 1 (2017): Article 4. DOI: http://dx.doi.org/10.5038/ 1936-4660.10.1.4