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Close. It's the cognitive bias where unskilled people greatly overestimate their own knowledge or competence in that domain relative to objective criteria or to the performance of their peers or of people in general.
Earlier quoted context omitted.
> therefore there would be strong a correlation between their performance and self-evaluation of said performance. If the data weren't related, then this hypothesis isn't likely, which is exactly what DK means. 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 heteroskedast…
> > 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. How does that not follow? It's just regression to the mean.
Earlier quoted context omitted.
Maybe another way of putting it is that the "Dunner-Kruger effect" is simply a tautology. Some formulation of it can still be true - albeit in a rather uninteresting way.
It’s not a complete tautology though, if people’s estimates of their skill were accurate in an unbiased way, we wouldn’t see a DK effect (or we’d only see a slight one, since you can’t really be unbiased at the low and high ends of the spectrum as the other comment pointed out). This isn’t true in the case of uniform random data, or in the real data we see, but it could be true of some data.
Very interesting article and statistical analysis, but I really don't see how it concludes that the DK effect is wrong based on the analysis. The fact that the DK effect emerges with _completely random data_ is not surprising at all - in this case the intuitive null hypothesis would be that people are good at estimating their skill, therefore there would be strong a correlation between their performance and self-eval…
This article seemed very unconvincing -- and this part noted above, early on in the article set the tone that I felt like the author didn't know what they were doing. And even after reading it all, I felt like the standard lay use of DK remained valid.
This just felt like the type of thing I would have thought about as an undergrad, started to write it, and then realized it didn't make sense halfway through it. Or maybe I just missed something as well...
It is unnecessary to walk the reader through autocorrelation in order to achieve a poorer understanding of that simple result.
Earlier quoted context omitted.
> What is the distinction between being bad at the skill and bad at estimation, and being bad at the skill and systematic overestimation? The difference between bias and variance. > conditioning on low skill and randomly sampling will tend to give way more overestimates than underestimates That's the hypothesis that's being tested.
> The difference between bias and variance. But when you're bad at the skill and can't underestimate, they look the same. > That's the hypothesis that's being tested And evidence from DK supports it.
The (definitional) difference between bias and variance isn't related to do with whether you're bad at the skill or not. It's just mean vs variance of a probability distribution.
If there's a good faith acknowledgement on your part that there's something here you're not getting then I'm very happy to try and help you understand it, and in the spirit of hn I'm assuming that is the case as you've claimed. I'm definitely not interested in any sort of motivated argument, though. If you're attached to the ideas you're putting forward here in some way I have no desire to try and dissuade you.
Operating on the former assumption, I'm not really clear where the misunderstanding lies at this stage, but perhaps it would help if you were to expand on in what sense you think being "bad at the skill" would make bias and variance "look the same"?
Earlier quoted context omitted.
> therefore there would be strong a correlation between their performance and self-evaluation of said performance. If the data weren't related, then this hypothesis isn't likely, which is exactly what DK means. 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 heteroskedast…
> 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)…
“If two variables are independent, then their correlation will be 0”
https://web.stanford.edu/class/archive/cs/cs109/cs109.1178/l...
“ If the variables are independent, Pearson's correlation coefficient is 0”
https://en.wikipedia.org/wiki/Correlation#Correlation_and_in...
Proof:
https://www.themathcitadel.com/uncorrelated-and-independent-...
This article is absolutely dripping with condescension throughout and is really pushing a "gotcha" that doesn't exist. It then argues basic statistics, generates a DK-looking graph from random data, and then claims the phenomena doesn't exist. When in fact, as other people have commented, when people are bad at estimating their own ability (i.e. random), the DK effect still exists; it falls out of statistics.
Sigh, the author misunderstood the very definition of the DK effect:
> "The Dunning–Kruger effect is the cognitive bias whereby people with low ability at a task overestimate their ability. Some researchers also include in their definition the opposite effect for high performers: their tendency to underestimate their skills."
In all the examples, this holds, even if the assessment ability is totally random. Even if every quartile gives themself an average score, like the random data generated here. The author seems to think that it should be even more lopsided or something to demonstrate the effect. (I mean, honestly, what are they expecting, a line above 50th percentile? A line with negative slope? What?)
If there were no DK effect, the two lines would be the same.
Instead, if we go back and look at the original data, we see indeed, the two lines are not the same, the average for the bottom quantile is over 50%, there is some small increase in perceived ability associated with actual ability (and not the opposite).
The sin here isn't some autocorrelation gotcha, but rather, DK should have put error bars on the graph. If it was totally random, the error bars would be all over the place.
Earlier quoted context omitted.
Not the D-K graph, the Nuhfer et al. graph.
The one reproduced in the article doesn't show the density of points, so it's hard to conclude anything from it. Figure 4 from the Nuhfer et al. paper does seem, to me at least, to support DK's conclusions.
Especially in a situation where you would expect the distributions to be of the same type ?
Unless they had messed up in failing to normalize the number of points per group, and so this might come from the law of large numbers failing + sheer randomness failing to create extreme values on higher-qualified, but lower population groups ?