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PaperclipPerfector  11mo ago (text post)   27875 thread views

Culture War Roundup for the week of November 27, 2023

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Pending a detailed read of Nufher et al. and Gignac & Zajenkowski, this appears as one of three -- either the blogpost is simply wrong; or I have a misundertanding of the Dunning-Kruger effect; or the author has done a really shit job at explaining himself. Either way, I'm not convinced so far.

It means that we can throw random numbers into x and y — numbers which could not possibly contain the Dunning-Kruger effect — and yet out the other end, the effect will still emerge.

My understanding of the DKE is that self-assessment is poorly correlated with objective ability in such a way that poor performers overrate their performance and good performers underate theirs. In this case, the lack of correlation in Fig. 7 from y being a variable with a uniform distribution uncorrelated with x already shows the effect! I'm not sure how the author is so sure that plotting uncorrelated variables and "showing" the DKE disproves it, as the entire point is that they're poorly-to-uncorrelated!

If my understanding of the Dunning-Kruger effect is right, I suspect the author may be right to some degree (just based on personal experience, I think DKE is extremely oversold, and even if true is unlikely to be very important), but his working is definitely wrong.

My understanding of the DKE is that self-assessment is poorly correlated with objective ability in such a way that poor performers overrate their performance and good performers underate theirs.

I think the point was something else. Imagine another test where people threw a dice and then they estimated what their dice throw was. Of course people who threw 6 could only underestimate or be correct and people who threw 1 could only be correct or overestimate.

So even if both the result and estimation was random, then you would reproduce Duning-Kruger effect due to autocorrelation. Result of “over/underestimation” is dependent and correlated to the measure you over/underestimate against which is also a variable. The correct answer is just that this is stupid statistical artefact.

vorelated

Kinky.

Avoid low-effort posts, please.

In that case I certainly did not get that impression from the blogpost, and I don't think this has anything to do with "autocorrelation" as much as it has to do with the data being bounded, which I think is another argument entirely. Incidentally I think the bounded-data explanation, which seems the most obvious one to me (along with other ones like the better-than-average effect and simple regression to the mean), are much more convincing than wrangling about autocorrelation. It's also using "autocorrelation" in a weird way.

I remain of the opinion that DKE is probably artefactual or minor at best, but the blog is still either poorly written or wrong.

My understanding of the DKE is that self-assessment is poorly correlated with objective ability in such a way that poor performers overrate their performance and good performers underate theirs.

This is not the "public consciousness" understanding of the DKE. That is the claim that "people who say they are real good and talk about how good they are are actually no better or even worse than the people who say they are bad".

poorly correlated with objective ability in such a way that poor performers overrate their performance and good performers underate theirs

This is not how poor correlation is usually defined either in real life, that's normally given by r, and you can have very high r while the statement "poor performers overrate their performance and good performers underate theirs" (say r = 0.99, an out of the world level correlation for anything in the social sciences) is still true.

This is not the "public consciousness" understanding of the DKE. That is the claim that "people who say they are real good and talk about how good they are are actually no better or even worse than the people who say they are bad".

In that case I stand corrected. That seems silly.

This is not how poor correlation is usually defined either in real life, that's normally given by r, and you can have very high r while the statement "poor performers overrate their performance and good performers underate theirs" (say r = 0.99, an out of the world level correlation for anything in the social sciences) is still true.

Well, of course "can have very high r and still have [that statement] be true" is true with r<1, especially since the data is bounded and poor performers are naturally going to be more room to overestimate and good performers underestimate. I thought the point of DKE was that r was low,

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