What Blair Fix's article gets wrong is that there are two stark differences between what Fix generated with random data and what Dunning and Kruger observed in theirs.

Fix has each person guess randomly between 0 and 99 where they will lie in the percentiles. They simulate every person having no idea and giving equal probability to being the best or the worst. If we then sort them by how well they really did into quartiles and then evaluate the average of how well they thought they would do, we get what we would expect: each quartile has an equal chance of predicting that they will do well or do poorly, with an average expected percentile of 50, which is what you would expect by a random guess.

Note two key things about this:
- All quartiles guessed the same - there was no correlation between what they guessed and how well they actually did
- All quartiles guessed the expected average percentile - 50%. This means they were unbiased in how well they thought they would do.

If people were unbiased but also unaware, this is the null hypothesis we would expect: on average people predict themselves to be average and there's no correlation between how well they predicted they would do and how well they actually did.

Now compare that to what Dunning and Kruger observed:
- The quartiles did NOT guess the same. There was a bit of an upwards trend, which suggests that people at least somewhat were able to determine their actual percentiles, even if only weakly on average.
- The predictions were biased. All groups estimated they would do better than the expected average. That is to say, on average, they thought they were above average. This is an important bias.
- The differentials between quartiles are not equal. The first and second quartile typically predicted the same, over-estimated value, implying that neither group had any idea they were better or worse than each other. However, the upper quartile consistently estimates a higher average. That is to say, people who perform well, on average, believe they are performing even better than those who don't perform well. And perhaps most surprisingly, there was often a statistically significant dip at the third quantile. Comparing their beliefs, people who did well believed they had done worse than the people who actually did worse.

Fix also fails to go beyond the first figure of the paper. After seeing this inconsistent behaviour between the quartiles, Dunning and Kruger then test what happens if the respondents are given an opportunity to grade each other - therefore getting an idea of what the percentiles actually look like - and to have their skills improved - thereby possibly making them better able to judge their own and each other's abilities. Again, if Fix's premise that this is all just a result of manipulating the autocorrelation of an otherwise unbiased random sequence, then these interventions should have no discernable effect. Yet, Dunning and Kruger find markedly significant changes after these interventions, and those changes are different within the different quantiles.

It is precisely this difference between quantiles which is the Dunning-Kruger effect. Fix effectively makes their point for them by building a null model and showing what would happen if there were no Dunning-Kruger effect - if people were fully unaware and unbiased. Instead, it is the way in which Dunning and Kruger's observations deviate from this model that is the very effect that bears their name.

Instead, all that Fix manages to do is point out how confusing the plot is that Dunning and Kruger produced. The plot can easily be misinterpreted to suggest that it's the difference between y and y-x that is important. Instead, in their writing, Dunning and Kruger actually focus on the differences in how y-x changes when the situation changes, demonstrating that it's actually dependent on knowledge and how different people respond to that knowledge. What they actually show is that delta(y-x) vs x has a nonzero relationship and this is particularly interesting.

Perhaps if Dunning and Kruger had not included the example of perfect knowledge as a comparison, but instead included the example of unbiased and unknowledgeable that Fix produced as the thing to compare against, the Dunning-Kruger effect would be much better understood.

Further, both could benefit greatly from plotting and tabulating not just an average, but the overall distribution within each group. Fix should know that variance is just as important as bias. Even if all groups are biased in their prediction, differences in variance between each group indicates their confidence in their belief. Knowledge should help to reduce both bias and variance. A guess with high variance tells us little, while a guess with low variance tells us quite a bit. Even if all quartiles predicted the same average, we wouldn't fault those with little ability for guessing a high number if they did so with low confidence. On the contrary, we would expect people with high ability to be more confident (and correct) in the assessment of their ability.