IMO that explains why Dunning Kruger seems intuitively correct even if the conclusion they drew isn't actually correct.
How is this helpful? You won't know whether someone is "overestimating" their ability until you learn both their estimated and actual performance, at which point you don't need to guess whether they're "likely" to have poor actual performance.
I think you're misreading the point of my comment.
Another way to show this would have been to keep the auto correlation plot, but compare it to the same plot with statistical noise. With infinite random data, the expected value for self-assessment would be 50% score, regardless of actual score - a flat line through the chart. It would then be significant to find a non-flat line, as DK did.
It’s not inconceivable that with a smaller sample, you’d get come biasing, where lesser skilled people would over estimate, and higher skilled people under estimate.
The follow up studies seem to suggest there’s not really a bias like that, but that there is a “honing” of the general ability to estimate your own outcome, which makes sense.
> Although there is no hint of a Dunning-Kruger effect, Figure 11 does show an interesting pattern. Moving from left to right, the spread in self-assessment error tends to decrease with more education. In other words, professors are generally better at assessing their ability than are freshmen. That makes sense. Notice, though, that this increasing accuracy is different than the Dunning-Kruger effect, which is about systemic bias in the average assessment. No such bias exists in Nuhfer’s data.