[obastani]

I feel like this article is severely over-complicating the analysis. Looking at the original blog post [1], their key claim appears to be that "random data produces the same curves as the DK effect, so the DK effect is a statistical artifact".

However, by "random data", the original blog means people and their self-assessments are completely independent! In fact, this is exactly what the DK effect is saying -- people are bad at self-evaluating [2]. (More precisely, poor performers overestimate their ability and high performers underestimate their ability.) In other words, the premise of the original blog post [1] is exactly the conclusion of DK!

Looking at the HN comments cited [3] by the current blog post, it appears that the main point of contention from other commenters was whether the DK effect means uncorrelated self-assessment or inversely correlated self-assessment. The DK data only supports the former, not the latter. I haven't looked at the original paper, but according to Wikipedia [2], the only claim being made appears to be the "uncorrelated" claim. (In fact, it is even weaker, since there is a slight positive correlation between performance and self-assessment.)

So, my conclusion would be that DK holds, but it does depend on exactly what is the exact claim in the original DK paper.

[1] https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...

[2] https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect

[3] https://news.ycombinator.com/item?id=31036800

[saurik]

> I haven't looked at the original paper, but according to Wikipedia [2], the only claim being made appears to be the "uncorrelated" claim.

Is it that hard to actually check the original paper before bothering to make such a claim? The original paper explicitly claims to examine "why people tend to hold overly optimistic and miscalibrated views about themselves".

[hgomersall]

Yeah, the model is a simple linear model (which I've yet to see written down) with some correlation coefficient which is the unknown. Derive an estimator for that correlation coefficient, being explicit about the assumptions, then we can have a discussion. Until then it's all lots of noise. The raw data would help too.