> DK doesn't mean no correlation, it means inverse correlation. It's the correct analysis at the bottom that shows what no correlation actually looks like (at least no correlation in tend, there is heteroskedasticity).
Not sure I follow, inverse correlation between what? The analysis at the bottom (assuming you mean fig. 11) is too dense to show if there's a correlation or not (between skill and self-assessment bias), and looking at the relevant figure from the paper itself gives me the impression that there is a correlation.
>> a world in which people are very bad at estimating their own skill, therefore, statistically, people with lower skills tend to overestimate their skills, and experts tend to underestimate it.
> Be careful here, the conclusion you drew doesn't actually follow.
Can you elaborate? If X and Y are two independent random variables, X representing skill and Y representing self-assessment of skill, X and Y will be negatively correlated - this is exactly what the first part of the article is about, although from my perspective it's the author who's drawing the wrong conclusion.
>> y - x ~ x, this is called the residual plot
> You're giving x and y meaning that they don't have. In the article these are uncorrelated random variables - the plot of y-x ~ x will always look that way. That's however not the case if you're plotting y_hat - y ~ y_hat for a y_hat taken out of a model. That won't be a random variable in your setup.
Not following again. Other than calling x y_hat, and having y_hat be your own estimate vs. x be the subjects' estimate, what is the distinction? What do you mean by "the plot of y-x ~ x will always look that way" - what way? The shape of the plot will necessarily depend on the relationship between x and y.