So this is a case of the Dunning-Kruger Effect?

Very hard to understand the meat behind all the fluff of the article, especially as the simulation code is not available, and as the presented simulated and original graphs are effectively the same (I don't see a disagreement).

It's clear that the perceived curve will be differently sloped, as no one will evaluate themselves as the topmost or the bottommost percentiles, so the edges will be biased.

And if in both cases we draw differences between perceived and actual, we will get the same curve that everyone knows, biased or not.

It’s obviously real, at least as used in conversation, whether it meets some rigorous definition I’m sure there’s an out, but we’ve all known these people. With vibe coding they’re everywhere. Is this going to be a modern “begging the question” where everyone knows what you mean but someone pipes up that actually the technical meaning is different?

> The Dunning-Kruger Effect Is Probably Not Real

Self-deception by any other name is still self-deception.

The Dunning-Kruger effect also applies to smart people. You don't stop when you are estimating your ability correctly. As you learn more, you gain more awareness of your ignorance and continue being conservative with your self-estimates.

But overall I think real intelligence by definition requires empathy and humility.

One has to realize that we can't know the things we don't know, which includes the fact that we can't always trust our own beliefs and opinions because we might be relying on faulty or incomplete information, or we might be suffering from a mental health problem, whether we are aware of it or not.

"As a rule, strong feelings about issues do not emerge from deep understanding." -Sloman and Fernbach

Isn't trying to discount published research with a glorified blog post the Dunning-Kruger Effect in action?

Even if it isn’t true, it’s got the feeling of truthiness (1).

I don’t expect it to ever go out of the public consciousness. Like other things that were never real like Stockholm Syndrome I suspect it’s just stuck in the zeitgeist now.

1. https://en.wikipedia.org/wiki/Truthiness

Maybe the core reasons are buried in this, but the amount of empty statements makes it hard to find

The strict academic definition hasn’t followed the colloquial usage for a long time. Maybe ever:

If a specific novice is over-confident and out of their depth, we say “Dunning-Kruger”

If a specific is under-confident and performing better than their self-estimate, that’s not commonly considered Dunning Kruger, in the colloquial use. It’s called imposter syndrome, or not labeled at all.

The researchers aren’t really disagreeing with that. They found that novices had a wider range of self-estimates of their performance than experienced people. So in the novice group you were more likely to find someone who was grossly over-confident in their abilities, but you also found people who underestimated themselves.

> instead showed that both experts and novices underestimate and overestimate their skills with the same frequency. “It’s just that experts do that over a narrower range,” he wrote to me.

Which doesn’t precisely contradict the idea that among novices you can find people who overestimate their skills. Which is how it’s commonly used.

So I can believe it’s a statistical wash when averaging across all subjects. But I never considered the common use of Dunning-Kruger to be applied to averaged groups of people. It was always brought out for those outliers on the long tail of the novice grout who thought didn’t even know what they didn’t know.

It is absolutely real.

Replication crisis. More than half of all psychology studies are not reproducible.

I'm at the point honestly, where I don't even consider psychology to be a science anymore.

The article may not take into account the possible effect of knowing about the Dunning-Kruger effect (or cultural sayings that goes in a similar direction) may bias measurements. Before it was widely enough known it was not a factor.

Also, negative knowledge comes in two flavours, what you know that you don't know and what you don't know that you don't know. There it may be ground for that effect, but also changes in culture may affect that, specially with exposure to internet/global culture and attitudes, that may make you more aware of what you don't know, and stories of success/fail for taking the wrong approach.

This article does not make it's case. He shows a graph of "random" data, and then just kind of keeps going. But that random data is the meat of the whole thing.

Cut out 60% of the useless text, and focus on explaining why random data should look like that.

I think what people need to realize is that the Dunning-Kruger effect is mostly "not real" because, on average, everyone (regardless of competence) overestimates themselves. Saying that incompetent people overestimate themselves doesn't prove Dunning-Kruger is real, because it doesn't negate the fact that competent people also do this.

Having worked in tech my entire life, no amount of research will convince me that the Dunning-Kruger effect is not real. You might as well tell me that this isn't air that I'm breathing.

But what if that is the Dunning-Kruger effect?

Regardless of whether Dunning-Kruger is real the solution is the same. DK concerns poor performing people who cannot accurately address their performance relative to a group. Forget DK. The bigger problem is missing objectivity, which is a very real concern. So, just measure for objectivity.

Can they measure things or do they just guess? Are they willing to seek evidence? Even if evidence is immediately available will they use it? Everybody has bias, but is their bias primarily self-oriented?

The consequences for poor objectivity are profound and measurable, but then its an invisible failure for people that struggle with this in the first place. In many industries poor objectivity can result in termination, law suits, criminal penalties, physical harm, and more. Software just seems to pretend this is vapor.

While the pop-culture notion of the Dunning-Kruger Effect is "idiots don't know they're idiots," the actual results of the paper were (essentially) that F students thought they were D students, whereas the A students thought they were B students. The argument here seems to be that the original effect is explained as essentially a kind of reversion of the mean argument (people assume themselves to be more average than they are), but I don't entirely buy that--especially since the simulation results they present don't really look like the original Dunning-Kruger results, since the crossover point is in the wrong place, and that's actually kind of significant in the original analysis...

There are so many strange things about the original Dunning-Kruger plot. Why use quartiles for one axis and percentile for the other? Why use higher precision for the subject's estimate, which is by definition imprecise, and lower precision for the true score, which is known precisely?

I think the only conclusion you can draw from that plot is everyone thinks they'll be in the third quartile.

Maybe this is reveals more about me than anyone else but the whole usage of dunning-kruger is just another arrow in the quiver for media to talk down to a group that they dismiss because they have different priorities.

I find references to the effect in pop culture are almost always used in an insulting, smug manner.

Excessive willful/unwillful ignorance is the root cause of someone exhibiting the Dunning-Kruger Effect. We've all at some point worked or lived with someone with real illusions/delusions about their abilities, and the root of it is ignorance. There's little we can do in our workplaces to mitigate these people. Word of advice from my experience: Never co-found a vc-backed software startup with someone that's done genuine innovation....and been completely ignorant and oblivious about everything else.

I think it's mostly misapplied. The best example of Dunning-Kruger is an intelligent, competent, Ph.D. in physics thinking 9/11 was faked because "jet fuel can't melt steel", not realizing that steel loses significant tensile strength as it heats up without necessarily melting, which I think most engineers would be aware of. His great knowledge in one area blinds him to his woeful lack of knowledge in another.

Damn if only this article actually explained why you see this effect from random data. Unfortunately it doesn't seem like they understand the maths enough to know. Does anyone fancy reading those papers and giving us a TL;DR?

I'm 100% confident it's real but I have no expertise on the subject. Checkmate, Atheists.

I've always found it somewhat ironic that the people who are least familiar with the actual research on the Dunning-Kruger effect tend to be the most confident in discussing it.

It's a sort of recursive Dunning-Kruger effect.

I mean, we all remember the cases where it was true, but do you really think most people think they are good at computer programming? Or speaking Russian? Or playing the harp? Or gardening? In the vast majority of cases, people who are not skilled at something, know that they are not. There are, sure, a few people who are overconfident, but the D-K effect as generally used in conversation was always pretty obviously untrue.

The key claim: "Random data actually mimics the effect really well."

This makes some sense. If people are asked to guess a number between 1 and 6 and then roll a die, the people who roll low are more likely to overestimate and the people who roll high are more likely to underestimate. But the key is precisely how well random data mimics the effect.

Hm... I vaguely remember a different article debunking the Dunning-Kruger. Basically the conclusion was that the data from the experiment shows that people's estimations of their results are all over the place, with people scoring high being actually slightly (but only slightly) more confident that they did well.

UPD: probably this one https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...

The article in the post is older though

The plot in the blog post seems to be very symmetrical around 50% (to the point where there must be some identity going on). The plot from the paper seems to cross over around 75%. So the 3rd quartile still has some explaining to do, right?

Is any of the raw datasets of such an experiment available? I would like to see a scatter plot of self-assesed score vs actual score instead of the data aggregated into four bins.

I feel like the only important point in the article would be to explain how the random data was generated, yet it was relegated to the single sentence: "There was no bias in the coding that would lead these fictitious students to guess they had done really well when their actual score was very low."

Because on the surface, it doesn't make any sense for two sets of "random" numbers between 0-100 selected in pairs to deviate from each other based on whether the first number in the pair was low or not. You would not expect the first number chosen in a pair to influence the second number. Whether the first number was between 0-25 or 76-100, you would expect the second number to be about 50.

So this is obviously some sort of structured randomness that may be entirely justifiable, but the only way to find that out would be to read the two articles that this article purports to summarize for the layman. Instead there's over 1300 words of slop before this sentence, then nearly 700 words of slop after this sentence. Turns out we don't need AI for this. Speaking of random, I don't think that 2000 words is random.