[larwent]

Unless I missed something, this article doesn't explain WHY random data can result in a Dunning-Kruger effect. The relationship between the "actual" and "perceived" score is a product of bounding the scores to 0-100.

When you generate a random "actual" score near the top, the random "perceived" score has a higher chance of being below the "actual" the numerical below is larger than the one above, and vice-versa. E.g. a "test subject" with an actual score of 80% has a (uniform random) 20% chance of overestimating their ability and an 80% of underestimating it. For an actual score of 20%, they have an 80% chance of overestimating.

[once_inc]

A person with an actual score of 80% will probably have enough confidence in his or her abilities due to experience that they will tend not to rate themselves low. Imagine being a graduate student asked how high (s)he would rank. They would not rank themselves as low as they might have when they were sophomore students. They would probably rank within 20% of their actual score, which is what the final graph in the article shows; professors have enough experience to be able to self-assess themselves better than less experienced subjects can.

[ImaCake]

As explained in the article, the reason is autocorrelation. Basically the y axis is correlated to the x axis because the y axis is actually x + random noise. The dunning kruger graph is then a transformation of that data - still subject to autocorrelation.