[ketozhang]

The autocorrelation is important to show that it's transformation to D-K plot will always give you the D-K affect for independent variables.

However, the focus on autocorrelation is not very illuminating. We can explain the behaviors found quite easily:

- If everyone's self-assessment score are (uniformally) random guesses, then the average self-assessment score for any quantile is 50%. Then of course those of lower quantile (less skilled) are overestimating.

- If self-assessment score vs actual score are dependent proportionally, then the average of each quantile is always at least it's quantile value. This is the D-K effect, which is weaker as the correlation grows.

-The opposite is true for disproportional relation.

So, the D-K plot is extremely sensitive to correlations and can easily over-exaggerate the weakest of correlations.