It still seems to me like "The DK Effect is Autocorrelation" is basically correct. The important thing isn't whether or not independence should be the null hypothesis, because calling something a "null hypothesis" is just an arbitrary label that doesn't affect reality. The important thing is that what we can actually conclude from the Dunning-Kruger paper is a lot less than popular presentations of the concept claim. In particular, "more skilled people are better at predicting their own performance" is really not supported by the paper, since that's not true of random data, which has everyone being equally terrible at predicting their own performance. If the random data can reproduce that graph, then the graph can't be proof that more skilled people are also better predictors.
Anyway, "The DK Effect is Autocorrelation" definitely seems to be both statistically literate, and a good faith criticism of the Dunning-Kruger paper. In light of that, calling it "anti-scientific" seems unfair, since criticism and debate are an important part of science.
> calling something a "null hypothesis" is just an arbitrary label that doesn't affect reality
It does affect your conclusions though.
The choice of null hypothesis in "The DK Effect is Autocorrelation" determined how the random data was generated. The hypothesis is: "nobody has any clue whatsoever how competent they are". The random data was specifically crafted for that hypothesis.
The choice of null hypothesis in this article is: "everyone roughly knows how competent they are". This random data, too, is specifically crafted for the null hypothesis.
So what does this mean? If you pick the a particular null hypothesis then you can try to argue that the DK is a statistical artefact. But it's not, it is an artefact of choosing a particular null hypothesis.
No, nulls matter a great deal. If you want to test a claim in Null Hypothesis Statistical Testing, the "significance" of the claim is in direct reference to the null. Changing a null will change the significance of the alternative. My favorite statement of this is from Gelman:
> the p-value is a strongly nonlinear transformation of data that is interpretable only under the null hypothesis, yet the usual purpose of the p-value in practice is to reject the null. My criticism here is not merely semantic or a clever tongue-twister or a “howler” (as Deborah Mayo would say); it’s real. In settings where the null hypothesis is not a live option, the p-value does not map to anything relevant.
Anyway, "The DK Effect is Autocorrelation" definitely seems to be both statistically literate, and a good faith criticism of the Dunning-Kruger paper. In light of that, calling it "anti-scientific" seems unfair, since criticism and debate are an important part of science.