If we assume random data then the people at the lower end will over-estimate their own performance the same amount that people on the higher end will under-estimate theirs. However, if the under-performers consistently over-estimate more than the over-performers under-estimate there is still some merit to the effect, isn't there? That is, the interesting number is the difference between integral of y-x on lower half…
I confess that I've never paid that much attention to the classic D-K graph, and that taking a close look at it, it is most assuredly crap. Now I want to know what the plots of the actual scores for those quartiles look like rather than %ile, or after-the-fact ranking. Yeah, it sure looks like people mostly figure they're in the 55-75 %ile ranking, if that's what that actually is, and that where in that spread they think they are correlates with their actual ranking.
Let's go down a Bayesian rabbit hole. Let's assume, as does the article, that people's self estimations are completely random rubbish: the worst people have nowhere to go but up, the best nowhere but down. Yup, completely agree.
Now let me ask a question: is self-estimation of any use in determining actual ability? The answer in this case is no: knowing one does not inform our ability to know the other in a Bayesian sense, they are not correlated.
D-K sounds valuable as a cautionary tale concerning excessive exuberance and a tendency not to learn well from experience, but aside from child-proof caps and Mr. Yuk stickers where we really want to apply the lesson is at the high-performing end of the scale and here we get into trouble immediately.
It is tempting to say "high-performers have nowhere to go but down" as though maybe we should reject those self-reporting the best performance. The classic chart hints at high performers underestimating their true performance, but it's a crappy chart; maybe they want it to be true.
But in the specific case where there is utterly no correlation and true performance is as evenly distributed as self-assessment, if we chop off the "top X self-reporting" we will chop off just as many poor performers as high performers. Yes, I hear you, and I agree, random is an edge case; I just don't believe that affects its prevalence.
Maybe it is true; alright dust off those priors and have at it.