I think Dunning Krueger makes intuitive sense. When you become skilled in your field you learn from other people in your field, and your assessment of yourself is based on your relation to the skills of those other people. But if you know very little about something, you have no reference point to evaluate yourself against.
When you learn something you also learn what are some of the mistakes you can make. You evaluate your performance then against the mistakes you didn't make. Consider a piano player, or figure-skater. You have to know about what figures are difficult to perform to evaluate a performance, and you don't know what the difficult ones are until you have studied and tried to perform them.
I think that's assuming more ignorance than even an unskilled person has.
If you had never listened to a professional play piano before then you'd have no idea what level of performance is possible. Similarly, if you had never seen skilled skaters perform on TV.
But we have done these things, so it's obvious that they're doing something that's very difficult.
Maybe you don't fully appreciate the skill, though. You wouldn't do well as a judge who compares the performances of professionals. But comparing novices to professionals seems easy?
The OP article mentions in their rumination that there's some difficulty in generalising DK:
"And maybe there’s no contradiction - there’s always room for nuance, for finding out where the Dunning-Kruger effect is relevant and where it’s not. That can be done with more studies, but only if the authors manage to agree on assumptions and basic statistical practice."
If you had never listened to a professional play piano before then you'd have no idea what level of performance is possible. Similarly, if you had never seen skilled skaters perform on TV.
But we have done these things, so it's obvious that they're doing something that's very difficult.
Sometimes the things we find most impressive, in a demonstration of a skill we don't have, aren't the most difficult things.
I remember being absolutely blown away by some aerial circus tricks and stunts I saw at shows. Later, I started studying and eventually performing myself, and it's often the case that the most crowd-pleasing stunts are some of the easiest to perform.
As a performer, you could always tell which members of the audience knew their stuff, because they'd be the only ones applauding the tricks that might not have looked so spectacular, but were actually the most difficult.
It's more like intermediate (most vulnerable to the DK-effect) to advanced (utmost appreciation for professionals).
Taking the piano example: after 1-2 years of progressive learning you can certainly give off the impression to somebody unfamiliar/untrained (including yourself to an extent) that you are actually quite good: Intermediate stage.
But after awhile when confronted with more and more challenging stuff, by discovering different styles and finetuning your hearing; you at some point reach the very visceral and uncanny sensation of the countless possible roads you can now explore: advanced stage.
>Similarly, if you had never seen skilled skaters perform on TV.
then, as a person who has lived in the world and has the normal physical skills of such you probably think "whoa, how in the heck did they do that" when you finally see it.
Your post reminded me of one of my favorite Adam Savage videos where he touches upon this idea you're exploring. I encourage folks to see it, he articulates it so well.
I linked to the start of the video where he begins to build the idea. TLDR is he mentions Monet painting Impression Sunrise and how it was something that people have never seen before and it took a bit of time for it to blow people away--they needed to develop "new eyes" to see the genius. Adam then dives into this idea of "new eyes". I'm sure many of us have experienced this in our life and it was so nice to hear Adam unpack it.
If human cultures can be characterized as default arrogant or default humble then it stands to reason that arrogant cultures will have a DK effect, and in humble cultures you won't.
I think you have the common misconception about the DK effect, which is incorrectly summarised as "unskilled and unaware".
There is also the other end of the scale where "skilled and unaware" occurs: people under-assessing their skill (presumed that this is due to judging that most people also have similarly high skill levels).
I think your two "cultures" would shift the self-assessment line up or down on the graph (constant), but not affect the slope very much (multiplier). The line shape or line slope must change somewhat since values are limited (between 0 and 100).
Even people conditioned to be humble could have a strong motivation to believe something is true and overestimate their own knowledge/ability in order to stand on what they perceive as evidence. For example a person's depression, religious beliefs, or an over-emphasized belief in DK itself could be a possible reason they have an erroneously deflated opinion of themselves, and simultaneously employ inflated confidence in irrational arguments that demonstrate why they are almost completely worthless at their field. That's pretty much how depression is secretly prideful in a sense: over-estimating our own mental ability to assess our helplessness.
When I did some cognitive behaviour therapy, I un-learned things like "all or nothing thinking" and the expectation that I could accurately predict the outcome of any course of action by modeling future performance off of a past failure.
> I un-learned things like "all or nothing thinking" and the expectation that I could accurately predict the outcome of any course of action
Do you know any words, stereotypes, or clichés for this? Or even what the related mental disorder is called if it were to become debilitating? Or a specific word for the complete clustering of related signals?
I am guessing those issues plus there related issues (¿syndromic?) are common - but I don’t know where to group it in my own mind.
It is closely associated with having a highly systematizing mind. People with ASD get drawn and pushed down a particular life history corridor. There are rewards of parental/teacher approval for high-performance in an area of profound interest, and a punishment in thef orm of peer bullying for low social skills. This conditions them to operate this way to avoid bullying and optimize for time seemingly well-spent with these impersonal systems. To justify one's own existence, there is this urge to live in a world where a narrowly focused mind is able to predictably produce an ideal world through expertise (all), and a tendency towards refusal to live outside of that, sometimes advancing into self destructive behaviour should that not be an available outcome (nothing). Getting ALL is not only about having things one wants, it is also about seeing the system work, and about identity, a sense of vindication.
Collective layer:
We see so much of it I think even among neurotypicals because we live in an extremely systematized world. Every single aspect of our world is seen by the "haves" of our society as a candidate for profitable separation through a digital layer. Anyone can end up metastasizing a systematizing mind. They just need to exhibit a hyper-focus on something impersonal and complex. As a global civilization, we have been doing this to ourselves and strapping others into it as much as we can. Mostly, only those living in rural parts of materially impoverished nations are spared this temptation.
Mental Symbolism layer:
It is symbolically speaking one entrance into a realm of mental death. With the devotion to lifeless systems the human is de-personalized, atomized, depressed, unrelational. They leave unfulfilled the inescapable truth of what it means to be human. To live your entire life this way is to betray your parents, ancestors, any friends or lovers you ever had, and anyone you could have helped.
Spiritual layer:
The pattern is quite literally satanic. The person has chosen to reign in this hell (both all and nothing), rather than serve in heaven (the humble, narrow middle path). The most powerful and beautiful of all created entities, with an astonishingly powerful mind, insisting on dwelling in a state of supreme perfection betrays the Father to become an engine of extinction.
No. I was just curious, and seeking understanding. I have friends that have told me their detailed future plans with exact timeframes and no contingency. I have seen others struggle to rationalise unpredicted forcing events in their lives - especially negative emotions when other people do not act according to their plan.
I think it's actually the next step; to get good at something you have to get good practice, and that requires good self-assessments and knowing how to practice the part you're weak on.
There are people who can't advance because they can't see the problem, and people who can't advance because they can't (or don't want to) correct it. The end result is the same though.
It's also intuitive if you think about error in self assessment. Skill is asymptotic to some upper bound. The closer to the asymptote (higher skill), then most likely the error in estimation is under it, since it cannot be above it.
Conversely it cannot also be under zero, so error is most likely going to be above the actual skill line (over estimation, since it's clamped below it).
Most people can't play the piano or skate. Don't consider those ones. Consider the things that everyone can do. Let's pick driving a car. I am fairly convinced that most people feel after a few years they are excellent at driving their car but in fact they are just OK to terrible. And this is with a lot of practice!
"The second option conforms with the Research Methods 101 rule-of-thumb “always assume independence.” Until proven otherwise, we should assume people have no ability to self-assess their performance"
It's not that at all. The assumption should be that everyone is equally good (or bad) at assessing their performance. Not that they have no ability but that the means between groups is the same vs. not the same. That the ability to assess themselves is independent of performance.
This confused me at first too. The issue is that "X" is your performance, and "Y" is your perceived performance.
Say that everyone is equally okay at assessing themselves, and get within 0.1 of their actual performance (rated from 0 to 1). Then X and Y are going to be very correlated, as X - 0.1 < Y < X + 0.1. But X-Y will look like a random plot, since Y is randomly sampled around X.
The only case where X and Y wouldn't correlate at all is if people have no ability to assess their performance (IE, Y isn't sampled around X, but is instead sampled from a fixed range).
Even though I lack a medical degree I have a high degree of confidence that the intestines of most people are not sufficiently large enough to support that amount of faeces.
It seems like the people who want to disprove Dunning-Kruger are falling victim to it.
I honestly think people take it way too seriously and apply it too generally. Quantifying "good" is hard if you don't know much about the field you're quantifying. Getting deep into a particular field is humbling -- Tetris seems relatively simple, but there are people who could fill a book with things _I_ don't know about it, despite playing at least a few hundred hours of it.
Is there an answer to that humility gained by being an expert in one field being translated to better self-assessment in other fields? I feel myself further appreciating the depth and complexity of fields I "wrote off" as trivial and uninteresting when I was younger as I get deeper into my own field (and see just how much deeper it is too).
> Is there an answer to that humility gained by being an expert in one field being translated to better self-assessment in other fields?
I think that often the opposite is true: people who become experts in one domain often assume that they are automatically experts in completely unrelated fields. I suspect that this is the cause of "Nobel disease": https://en.wikipedia.org/wiki/Nobel_disease
Imagine in the Dunning-Kruger chart the second plot (perceived ability) was a horizontal line at 70, which is not true but not far off from the real results. Now imagine I told you "did you know that, regardless of their actual score, everyone thought they got a 70?" That's a surprising fact.
> Most people have an above-average number of legs.
The arithmetic mean and the median are both averages, but the upthread comment was about the median and yours about the arithmetic mean.
> There's really no contradiction there; all it takes is for there to be a couple low scores pulling the average down.
Well, no, when what you are estimating is relative performance by score percentiles, and people's self evaluation is biased toward the 70th percentile, that's not what is happening.
I think the most egregious thing about the original presentation is that it leads you to believe that people with a given skill level all self-assessed similarly. If you plotted the scores and self-assessments of each individual you would see that it's not "everyone [in the first quartile] thought [they were about average]", it's that their self-assessments varied wildly, from low and accurate to high and inaccurate.
The corollary of Dunning Kruger is that everyone is equally capable and equally capable of assessing their performance. This nicely suits the current social rhetoric but does not match observed reality.
The less you know, the more random your guess at your own knowledge is. The actual value is low and less than zero isn't an option, so this drags the average up consistently.
The more you know, the more accurate your guess of your knowledge is. Especially as you hit the limits of the test, this noise can only drag the average down, but less dramatically than the other case.
With the reasonable conclusion: We all suck at guessing how much we know, but the more you know the less you suck until you hit the limits of the framework you are using for quantization of knowledge.
Assuming a world where all the participants understand normal distributions, would this be addressed by asking people to rate how they did in terms of "standard deviations compared to the average" or such?
That still wouldn't be useful. The root problem is that a scoring system that isn't infinite both ways (or the reasonably achievable scores are significantly farther from the bounds than the variance of guesses) will end up with a "clipping" at the edges of the model.
There are ways to fix this:
- Throw out the extreme high and low ends of the data bc the model breaks down there. (Which results in a very boring result)
- Have people guess their score and a rough level of confidence along side it (just a 0-5 sort of thing) and see what happens.
Note that I actually do think from my own experience that the effect is real, but the arguments presented fail to prove it statistically bc the model breaks down at the extreme where the effect is detected.
I had the same thought while reading this. The test has a limited range of values, you can only estimate your score within that range, no higher or lower. Those at the top and bottom will naturally estimate into the body of the range since a lower or higher estimate is not possible. However, I’m not sure this explains the results entirely, and I’d like to see a statistician take this further.
That original article was bogus and needlessly combative. I feel like the majority view in the HN comments saw it as such.
Most comments were splitting hairs on what _exactly_ the Dunning-Kruger effect was, plus some general nerd-sniping on how the original article was off base.
IMO it was something that fell flat on its own rather than something that needed a lengthy refutation, but I can understand that sometimes these things get under your skin.
On a pure human level, a large portion of DK discourse seems to be a fight over which people are the "Unskilled and Unaware." Or more bluntly, who gets to call who stupid.
The author says as much in this article:
> Why so angry? [...] [Frankly], for the last few years, the world seems to be accelerating the rate at which it’s going crazy, and it feels to me a lot of that is related to people’s distrust in science (and statistics in particular). Something about the way the author conveniently swapped “purely random” with “null hypothesis” (when it’s inappropriate!) and happily went on to call the authors “unskilled and unaware of it”, and about the ease with which people jumped on to the “lies, damned lies, statistics” wagon but were very stubborn about getting off, got to me. Deeply. I couldn’t let this go.
> In their seminal paper, Dunning and Kruger are the ones broadcasting their (statistical) incompetence by conflating autocorrelation for a psychological effect. In this light, the paper’s title may still be appropriate. It’s just that it was the authors (not the test subjects) who were ‘unskilled and unaware of it’.
But on some level, the original paper sounds just as condescending and dismissive. It presents a scholarly and statistical framework for looking down on "the incompetent" (a phrase used four times in the original paper). In practice, most of the times I see the DK effect cited, it functions as a highbrow and socially acceptable way of calling someone else stupid, in not so many words.
Cards on the table, I've never liked DK discourse for this reason. It's always easy to imagine others as the "Unskilled and Unaware", and for this reason bringing DK into any discussion rarely generates much insight.
> it functions as a highbrow and socially acceptable way of calling someone else stupid
I think it's even worse that that: it's also a socially acceptable way of enforcing credentialism and looking down on others for not having a sufficiently elite education.
> Again, my main point is that there’s nothing inherently flawed with the analysis and plots presented in the original paper.
I find the use of quartiles suspicious, personally. It's very nearly the ecological fallacy[1].
> I’m not going to start reviewing and comparing signal-to-noise ratios in Dunning-Kruger replications
DK has been under fire for a while now, nearly as long as the paper has existed[2]. At present, I am in the "effect may be real but is not well supported by the original paper" camp. If DK wanted to they could release the original data, or otherwise encourage a replication.
If others can't replicate it entirely on their own without "encouragement". Then it isn't useful at all and the original experiment can be safely ignored as irrelevant to humanity, along with any "prestige" associated with it.
FWIW extreme groups (e.g. using upper and lower quartiles) is well understood in its inflation of effect size (there are even formulas to correct this, given an extreme groups design).
It's definitely related to ecological fallacy in the sense that both underestimate relative error and inflate effect sizes.
Agree. From the DK article graph it is not possible to separate the cases
1. Average self assessment coincides with true skill, but variance increases with low skill.
2. Average self assessment is biased, and the bias is positive when you are unskilled and negative when you're highly skilled.
These two situations would create indistinguishable DK-graphs. I don't understand how anyone can be sure on either (1) or (2) after seeing one instance of such a graph.
As I see it, the only way out for "DK positivists" is to say that the DK hypothesis is unrelated to the truth values of (1) and (2). Or, that there is other evidence making DK convincing.
What’s more interesting to me is what all the buzz over DK tells me. We are asymmetrically skeptical. In the same way as intelligent people doubt their own performance, they rightly doubt others’ performance. Maybe too much.
It's called a giant fucking lack of self awareness, with a good helping of societally instilled narcissism on the worst side of it all, and then add in imposter syndrome, self righteousness and gaslighting. The best side of it all basically is all of these things, but with a tight leash on things and sans the gaslighting. There might be better, but those people are probably off doing their own thing minding their own business; etc.
I think that most people who talk a lot about DK believe that they are the experts in one field or another.
It serves mostly as a way of reassuring themselves of their own superiority. The message (for them) basically amounts to "other people's claim to knowledge is just further proof that they don't know anything."
It’s a zero-effort, zero-evidence-required way for people to disparage others, in a way that they believe makes them sound smart. It’s also basically unfalsifiable in most of the cases where it’s referenced.
I feel like I’m honestly yet to see somebody make DK accusations in a way that’s not totally cringe.
> I think that most people who talk a lot about DK believe that they are the experts in one field or another.
I recall that either Dunning or Kruger once made a remark to that effect. That rather than an indictment of stupid people, it would be better to view it as a warning to those who consider themselves the smart ones.
I feel like there's a bit of a paradox here. The more I internalize how easy it is for us to be overconfident in our intelligence, the more confident I feel in my intelligence...
Any discussion of statistics-based reasoning should include the concept of systematic bias, and that's not mentioned in this article at all. An example of systematic bias is that of an accurate but miscalibrated thermometer, where the spread of measurements at fixed temperature is small, but all measurements are off by some large factor.
Now with D-K the proposed problem is statistical autocorrelation, not systematic bias, due to lack of independence, as here:
> "Subtracting y – x seems fine, until we realize that we’re supposed to interpret this difference as a function of the horizontal axis. But the horizontal axis plots test score x. So we are (implicitly) asked to compare y – x to x"
Regardless, it's fairly obvious that D-K enthusiasts are of the opinion that a small group of expert technocrats should be trusted with all the important decisions, as the bulk of humanity doesn't know what's good for it. This is a fairly paternalistic and condescending notion (rather on full display during the Covid pandemic as well). Backing up this opinion with 'scientific studies' is the name of the game, right?
It does vaguely remind me of the whole Bell Curve controversy of years past... in that case, systematic bias was more of an issue:
> "The last time I checked, both the Protestants and the Catholics in Northern Ireland were white. And yet the Catholics, with their legacy of discrimination, grade out about 15 points lower on I.Q. tests. There are many similar examples."
I am reminded of something my very accomplished PI (in the field of earth system science) confided privately to me once... "Purely statistical arguments," she said, "are mostly bullshit..."
> Regardless, it's fairly obvious that D-K enthusiasts are of the opinion that a small group of expert technocrats should be trusted with all the important decisions
It seems like you're roughly the only person who thinks this.
I think the main point of this post is correct -- just because you can find the effect in random noise, doesn't mean it's not real phenomenon that happens in real life. But it's missing a nuance there: if an effect can be replicated with random noise, then it's not a psychological effect (e.g. something that you would explain as a human bias), but a statistical effect. E.g. regression towards the mean is a real effect, but it's a statistical effect, not a psychological effect.
And that's the point the original article was trying to make ("The reason turns out to be embarrassingly simple: the Dunning-Kruger effect has nothing to do with human psychology. It is a statistical artifact — a stunning example of autocorrelation."), though that point does lost a bit as it goes on.
> if an effect can be replicated with random noise, then it's not a psychological effect
This isn't true either. Statistical dependence does not determine or uniquely identify causal interpretation or system structure. See Judea Pearl's works (e.g. The Book of Why) for more on this.
People lacking the ability to self-assess is interesting psychologically. People can learn from experience in many other contexts. People can judge their relative position versus other people in many contexts. Why would they be so bad at this particular task? There could be a psychological underpinning.
Even if it turns out we have useless noise-emitting fluff in the place that would produce self-awareness of skill, that would be a psychological cause of a psychological effect. Not the ones that Dunning and Kruger believed they were seeing, but still.
Now, if you asked frogs for a self-assessment of skill, I would expect that data would not show any psychological effects.
"Noise" isn't a generic concept that can be universally applied in the same way in any scientific context. That's the point of the article. It doesn't make much sense to assume that people's self-assessments come from an internal random number generator.
It makes sense as a robotically developed null hypothesis, but it doesn't make sense in the real world.
> People lacking the ability to self-assess is interesting psychologically. People can learn from experience in many other contexts. People can judge their relative position versus other people in many contexts. Why would they be so bad at this particular task? There could be a psychological underpinning.
Is there an existing and relevant phenomenon about people lacking the ability to self-asses, that is true, proven, and not just trivia?
I do believe that people understand all the available information about their skills and performance, and they rate themselves according to it.
E.g. if they are asked about whether they perform good on an IQ test against their classmates they will produce noise (see E.g. the article "I can't let go of.."), and if they have the results of the IQ test, they will be able correctly calculate in which quartile they are.
I have no idea. I think it would be a fascinating experiment to take people who have never taken an IQ test, ask them how they think they'll do, then compare that against their actual performance.
To riff on one of the author's previous comments, if height was uncorrelated with age for 0-20 year olds, that would be very surprising, and hopefully we wouldn't need to make posts saying "the fact 20 year olds are just as likely to be 1 ft tall as 1 year olds is not a physical phenomenon, it's a statistical effect."
I feel like this article is severely over-complicating the analysis. Looking at the original blog post [1], their key claim appears to be that "random data produces the same curves as the DK effect, so the DK effect is a statistical artifact".
However, by "random data", the original blog means people and their self-assessments are completely independent! In fact, this is exactly what the DK effect is saying -- people are bad at self-evaluating [2]. (More precisely, poor performers overestimate their ability and high performers underestimate their ability.) In other words, the premise of the original blog post [1] is exactly the conclusion of DK!
Looking at the HN comments cited [3] by the current blog post, it appears that the main point of contention from other commenters was whether the DK effect means uncorrelated self-assessment or inversely correlated self-assessment. The DK data only supports the former, not the latter. I haven't looked at the original paper, but according to Wikipedia [2], the only claim being made appears to be the "uncorrelated" claim. (In fact, it is even weaker, since there is a slight positive correlation between performance and self-assessment.)
So, my conclusion would be that DK holds, but it does depend on exactly what is the exact claim in the original DK paper.
Yeah, the model is a simple linear model (which I've yet to see written down) with some correlation coefficient which is the unknown. Derive an estimator for that correlation coefficient, being explicit about the assumptions, then we can have a discussion. Until then it's all lots of noise. The raw data would help too.
> I haven't looked at the original paper, but according to Wikipedia [2], the only claim being made appears to be the "uncorrelated" claim.
Is it that hard to actually check the original paper before bothering to make such a claim? The original paper explicitly claims to examine "why people tend to hold overly optimistic and miscalibrated views about themselves".
The "The Dunning-Kruger Effect is Autocorrelation" article is an example of obvious bullshit.
Their claim that "If we have been working with random numbers, how could we possibly have replicated the Dunning-Kruger effect?" is the first blatantly false statement, and then the rest is built upon that so it can be safely disregarded.
It's easy to see this because while the effect is present if everyone evaluates themselves randomly, it's not present if everyone accurately evaluates themselves, and these are both clearly possible states of the world a priori, so it's a testable hypothesis about the real world, contrary to the bizarre claim in the paper.
Also, the knowledge that the authors published that article provides evidence for the Dunning-Kruger effect being stronger than one would otherwise believe.
Your comment amounts to saying that some of the randomly generated data really is consistently over estimating it's performance. How absurd.
Like similar analyses here you don't factor in that DK is about bias. Of course you can't see bias when test score=self assessment. That's because "IF everyone perfectly knows their score then there is no bias in their assessment" is a tautology.
tldr; D+K's experiment was: Assign the numbers 1 thru 10 to ten people. Have each role a 10 sided die. The person assigned a 1 will roll higher than his assigned number 90% of the time.
Daniel:
>It’s not a “statistical artifact” - that will be your everyday experience living in such a world.
You can experience statistical effects. I think a lot of controversy comes from how Dunning and Kruger's paper leads people to interpret the data as hubris on the part of low-performers, and the statistical analysis demolishes that interpretation. Not knowing how well you performed is not the same thing psychologically as "overestimating" your performance.
Dunning Krueger is precisely about the surprising result that people are bad at estimating their performance!
If you accept the 'D-K is autocorrelation' argument, you don't get to throw out the existence of the D-K effect: you are saying Dunning + Krueger failed to show that humans have any ability to estimate how skilled they are at all.
That seems like an even more radical position than the D-K thesis.
> Dunning Krueger is precisely about the surprising result that people are bad at estimating their performance
Isn't DK about estimating your performance relative to the rest of the population? To do that, you need to not only know your own performance but also everyone else's. To me, guessing the performance of others sounds quite difficult.
The implicit hypothesis is that if a test is full of questions you have no idea how to answer, you really ought to have strong priors that you're a below average performer. Tests are generally designed so that people familiar with the relevant material and methodologies can attempt answers; you dont need to know exactly how good other test takers are for it to be reasonable to assume you're in the bottom quartile if you can't. Same as you should have a lot less difficulty than most cyclists estimating whether your time trial was a good one relative to the rest of the field if you struggled to stay on the bike.
Of course, there are tests where the bottom quartile find the majority of it easy and have no particular reason to assume that most others found it even easier, and circumstances in which the weak undergrad who can only answer half the questions may reasonably believe that the test is being administered to a general population full of people who won't understand any of the material at all. But in general, it's reasonable to assume that if there's a lot of stuff you don't know, other people will know better.
The claim that skill does not exist or that people are totally unable to recognize how good they are at anything is quite radical.
You are sort of smuggling in the assumption for example that Olympian medalist lifters, when asked how much they can deadlift, will have the same distribution of answers as people who never deadlift (but are aware that totally sedentary men can probably deadlift like 200lbs and totally sedentary women can probably deadlift like 150lbs). If this were true, it would be worth publishing a paper about it.
It's sort of surprising to me to read your comment because TFA is an extended rebuttal of your comment.
> I think a lot of controversy comes from how Dunning and Kruger's paper leads people to interpret the data as hubris on the part of low-performers, and the statistical analysis demolishes that interpretation. Not knowing how well you performed is not the same thing psychologically as "overestimating" your performance.
D-K actually found that low performers were less accurate at assessing their skill than high performers, and the article you refer to obviously did not find this effect in random data, so I'm not sure how it was demolished.
The plot to me always read "People estimate themselves at 60-70% percentile - above average, but not the best". And then given this broad prior, people do place themselves accurately(because the plot is increasing).
So it seems people are bad at doing global rankings. If I tried to rank myself amongst all programmers worldwide, that seems really hard and I could see myself picking some "safe" above-average value just because I don't know that many other people.
There's also: If you take 1 class in piano 30 years ago and can only play 1 simple song, that might put you in the 90th percentile worldwide just because most people can't play at all. But you might be at the 10th percentile amongst people who've taken at least 1 class. So doing a global ranking can be very difficult if you aren't exactly sure what the denominator set looks like.
So I think it's an artifact of using "ranking" as an axis. If the metric was, "predict the percentage of questions you got correct" vs. "predict your ranking", maybe people would be more accurate because it wouldn't involve estimating the denominator set.
This is exactly my conclusion, and it seems obvious... just look at the self assessment line - pretty much everyone thinks they are slightly above average. Once you know that everyone thinks they are above average, you already know how it will play out... the bottom quartile will have the biggest gap between actual skill and estimated skill.
> There's also: If you take 1 class in piano 30 years ago and can only play 1 simple song, that might put you in the 90th percentile worldwide just because most people can't play at all. But you might be at the 10th percentile amongst people who've taken at least 1 class. So doing a global ranking can be very difficult if you aren't exactly sure what the denominator set looks like.
Yes, and this literally implies that people in the lowest quartiles can't and won't rate themselves to be in the lowest quartiles when they are forced to give an answer. (Especially on tests that doesn't measure anything (getting jokes? really?), on tests that they have no knowledge about (how would they know that how their classmates perform on an IQ test???), or on tests that just have a high variance.)
And therefore they will "overestimate their performance".
It's like grouping a bunch of random people, and forcing them to answer whether their house is short, average or high. The "people living in short houses" will "overestimate the height of their houses", while the "people living in towers" will humbly say they live in an average high house.
Is this an existing and relevant psychological phenomenon, different from the general inability to guess unknown things? I don't think so.
One difference is that as the experiments were run on psychology students, they know the population, those are their peers with whom they interact on a daily level and they should have an idea of how they compare with them.
> how would they know that how their classmates perform on an IQ test???
Are you serious? If you're interacting with your classmates, you definitely should have some idea on how their intellectual capabilities differ between each other and also with respect to you. In a small class doing lots of things together, someone might even literally count their "ranking" at some metric that highly correlates with IQ, estimating that Bob, Jane and Mary are above me and Dan and Juliet are below me, so I'm at 40th percentile.
It's not appropriate to treat these aspects as unknown things or unknowable things.
One minor correction - the article creates a dichotomy where the hypothesis must be either
1) self-assesment is somewhat correlated with skill, or
2) completely uncorrelated
And this is a true dichotomy. The "autocorrelative" effect doesn't need perfect correlation, just some correlation.
> Why so angry?
I know I’ve taken this far too personally. I have no illusions that everything I read online should be correct, or about people’s susceptibility to a strong rhetoric cleverly bashing conventional science, even in great communities such as HN. But frankly, for the last few years, the world seems to be accelerating the rate at which it’s going crazy, and it feels to me a lot of that is related to people’s distrust in science (and statistics in particular). Something about the way the author conveniently swapped “purely random” with “null hypothesis” (when it’s inappropriate!) and happily went on to call the authors “unskilled and unaware of it”, and about the ease with which people jumped on to the “lies, damned lies, statistics” wagon but were very stubborn about getting off, got to me. Deeply. I couldn’t let this go.
I am afraid I actually agree with the author's point. The anti-intellectual, anti-scientific streak in many poor analyses claiming to debunk some scientific research is deeply concerning in our society. If someone is trying to debunk some scientific research, at least he should learn some basic analytic tools. This observation is independent of whether the original DK paper could have been better.
That said, I give the benefit of doubt to the author of "The DK Effect is Autocorrelation." It is a human error to be overly zealous in some opinions without thinking it through.
What about the replication crisis? It's possible to use rigorously sound statistics to lie (or at least unknowingly spread falsehoods). I can't tell you how many times I've seen headlines or abstracts of studies that seem to contradict ones I've seen previously, and back and forth! Particularly in the social sciences.
I recall one study that said all white people are committing environmental racism against all non-white people. I dove in and read the whole thing wondering what method could have yielded scientific confidence in such a broad result. Turns out the model used was a semi-black box that required a request for access and a supercomputer to run. But it was in a Peer Reviewed Scientific Journal and had lots of Graduate Level Statistics so I guess it seemed trustworthy.
A replication crisis indeed exists. All the more reason to analyze rigorously. Poor analyses (and borderline name-calling) in the original article do not help with the crisis.
>it's possible to use rigorously sound statistics to lie (or at least unknowingly spread falsehoods).
I don't think this is true. It is possible to put a lot of work into unsound statistics and to make a lot of "noise and fury" about how mathematical you are while failing some basic principle, but I don't think sound statistics can mislead. The replication crisis was caused by scientists not being rigorous and journals not forcing them to be. You absolutely cannot accept publication as a sign of sound techniques except in journal/field combinations that have a deserved reputation.
Of course they can, unless you magically exclude all statistics that made a bad assumption on independence.
I plot all the daily high temperatures and the presence of the ice cream cart and it turns out the ice cream cart causes warmer highs! Solid statistics.
Turns out the guy that has the ice cream cart has a weather app on his phone though and doesn’t come out on forecasted cold days.
Is that the fault of statistics though, or the non-statistical implication of causation that was tacked on the end of the statistical detection of correlation? Statistics is pretty explicit that it can't tell you about causality, right?
The issue here has not much to do with the replication crisis. It has to do with the fact that most people who use bits of information to make their point more convincing don't care whether that information is true or not. They are not seeking to convince the other side of the issue, they are seeking to convince other believers.
It is literally like this:
- someone makes a point that questions your believe
- you google a phrase that would come in studies that proof otherwise
- you take the first thing that looks promising, and fly over the first page, and paraphrase a good bit in a way that makes your point
- you publish it as part of a post, youtube video or whatever
- danger averted
Bad studies play into this, but even if the studies are good, or bad studies that have been retracted the same thing happens. James Wakefield who originally published the "combined vaccines cause autism study" after patenting a non-combined measles vaccine had his study retracted by the lancet soon after publication. He lost his status as a doctor etc. And you will still find people who use his study as a source.
Of course studies whose outcome collide with our believe systems are always harder to trust than those who validate it — but this is why you look at the methods used and other indicators that might make that study bogus.
> It's possible to use rigorously sound statistics to lie (or at least unknowingly spread falsehoods)
The book "How to lie with statistics" is one of the best statistics textbooks that I have read. It basically makes you immune to misleading stats (charts, tables, everything).
IIRC, the only thing that is missing from the book (it's a really old book) which is very relevant is p-hacking.
Given the explosion in the number of journals and the impossibility of effective peer review, being published in a journal does not mean what it used to. This is part of the material drivers for the replication crisis (journals can no longer effectively gatekeep scientific validity), but it also reflects something real about the practice of science: little social cliques come up with pet theories and, over time, "fight" with these theories on epistemic common ground. The successful ones, we'd like to think, are the ones that last the most rounds in the fight, but that probably only holds in the long run. Contradiction, in itself, is normal (and was before!)
Most social science is shoddy, fake, or otherwise misleading (i.e. it proves nothing meaningful despite the claims of the researchers). If you believed every social science study you heard about, you'd be more wrong about the world than if you disbelieved them all.
Let's not forget though that a great deal of "science" is in fact trash[1]. The problem isn't really people being anti-science or pro-science. The problem is science being done poorly, whether by scientists in the credentialed sense, or amateurs.
There is no pat "trust science more" or "trust amateurs less" answer here. The actual answer is that if you want to understand research, you need to actually understand mathematical statistics and the philosophy of statistics fairly deeply. There just isn't any way around it.
Completely agreed. While I strongly believe in citizen science and people's right (and perhaps obligation) to critique established science, there is just so much poor analyses done by people to criticize some scientific findings they do not approve, motivated emotionally or otherwise. This phenomenon does not bring us closer to finding scientific facts or resolving the replication crisis. People should learn some basic analytic tools first.
I can see why this has been downvoted, but I didn’t mean to sound like a luddite. I really just think there is a lot of low quality research, especially in the social sciences, done primarily to keep an publishing schedule up for pressured academics. We’d often be better to spend more time thinking about and planning fewer better studies.
I think there's two extremes here. One is the issue covered well above. There is a great deal of junk science that gets published. That is a problem and it does erode trust. But in some ways it's also how the sausage gets made, there's going to be room for things to get published they later gets refuted. People rightly so have distrust for results coming out in fields they don't have good knowledge in. Without becoming an expert yourself it's very difficult to know who or what to trust.
On the other end there's distrust of broad scientific consensus across different professions, countries, etc. It's the distrust at these levels that is the increasing problem we are facing today.
> People rightly so have distrust for results coming out in fields they don't have good knowledge in. Without becoming an expert yourself it's very difficult to know who or what to trust.
A problem here is that there are fields of science that are almost certainly bogus in themselves. One very likely candidate is nutrition, which seems to be fumbling in the dark and has a long history of producing worse recommendations than doing nothing (e.g. replace fat with sugar). More controversially, the entire field of economics is seen by some to be very suspect from a basic foundations view.
>A problem here is that there are fields of science that are almost certainly bogus in themselves. One very likely candidate is nutrition, which seems to be fumbling in the dark and has a long history of producing worse recommendations than doing nothing (e.g. replace fat with sugar).
It's not the field that is bogus in that case. People were quite literally bribed to push this. This could happen anywhere, anytime, in any field.
That was just an example, but we can go into more detail - at every point, nutrition studies are highly questionable. Sample sizes are usually minuscule (you can find important studies with 5-10 subjects, and even the largest studies rarely have more than a few hundred), they are often biased samples (only overweight people, only people with heart disease or diabetes etc.), they often don't account for likely confounding factors (using weight without accounting for muscle mass, no accounting for stress, time in the sun etc.) and on and on. And all of this in a subject where we basically don't have any clear idea about how much metabolism differs from one person to the next, based on what factors (e.g. gut microbiome has only been recently identified as a major component of digestion that can differ significantly between people; psychological effects of diet are even less well understood, even though your food choices are obviously not coming from some pure realm of reason).
Basically the digestive system is far too complex for us to understand from first principles at this time. Your diet has a very complex and very slow effect on your body, with some exceptions. Numerous diseases and environmental factors impact how this plays out exactly. So, to do real research in nutrition, the only chance right now would be to conduct massive studies over long periods of time with rigorous controls on subjects' nutrition and activities - which is basically impossible, or at least prohibitively expensive.
Instead, we get conclusions drawn from studies of a few dozen people over a few months or at best a year (in "long-term" studies). Or, we get conclusions drawn from comparing diet across huge populations ("the Mediterranean diet", "the Japanese diet", "the American diet" etc) with no possibility to control for obvious differences in nutrients, environmental factors, lifestyle differences, access to healthcare etc. Both of these are worthless conclusions, they don't tell you anything at all.
The only successes of nutrition science have been identifying the most basic nutrients we need to survive at a basic level (protein, fat, carbohydrates, and the various vitamins and minerals). Basically nothing beyond that should be trusted.
As a fun historical note, after the discovery of the macro-nutrients there was a budding field of nutrition scientists confidently recommending optimal diets using scientific methods. Unfortunately, they had no idea about the existence of micro-nutrients, so actually following some of their diets you could actually end up getting scurvy or other serious malnutrition diseases. The current slew is not that bad, but I wouldn't be surprised if in the future we will look back similarly at some common diet advice of today.
I think commercial operations like Huel and similar supplements will definitely be thought of like that. It just seems crazy, having watched the evolution of "scientific" diet advice over the past 70 years, to now think it's plausible that industrially-produced protein drinks are a suitable food substitute.
> I think there's two extremes here. One is the issue covered well above. There is a great deal of junk science that gets published.
It's more than that, I think. Sibling-thread poster hit the nail on the head when he complained of politicised science.
The social sciences have this dominating and silencing effect on the rest of the sciences.
There's always been junk science, and when found out it gets discredited. This is still happening and is a good thing.
What's new is that any research that might produce results counter to the what the PC-mob deems acceptable is attacked. Whether or not there is consensus amongst researchers in that field is irrelevant when the mob calls for the firing of any researcher who doesn't toe the current political party-line.
Sure, we're not actually in the dark ages, but a trend of silencing voices in the name of purity of thought is particularly troubling, especially as the mob asking for this is unashamedly attempting to implement NewsSpeak[1].
[1] See the argument in yesterdays threads about what "man" and "woman" mean, and should dictionaries be changed, etc.
No this is not new. You have always have a direction set by political views, even if we have decided they are wrong they are still hard to kill like: smoking is good, white people are superior. There is still "science" being done to bolster those political views.
>> What's new is that any research that might produce results counter to the what the PC-mob deems acceptable is attacked.
> No this is not new.
I don't recall a PC-mob being used to silence any and all non-supportive voices until quite recently.
> You have always have a direction set by political views, even if we have decided they are wrong they are still hard to kill like: smoking is good, white people are superior. There is still "science" being done to bolster those political views.
I don't see what that has to do with that I said - that a very vocal bunch of non-science people seem to have successfully lobbied into silencing specific topics.
Good science defines its terms. Can you unroll "PC-mob" so we're all on the same page here? You don't sound like an asshole, so your meaning is probably not the usual "anything that leans a little left."
The real problem is that all of those mobs have to answer to the PC mob now. Or at least that's what I hear from people who think that censorship didn't exist until coincidentally around the time of Gamergate.
> I don't recall a PC-mob being used to silence any and all non-supportive voices until quite recently.
Are you genuinely serious or were you completely unaware of anti-communist government sanctioned blacklisting of academics suspected of being communist for political clout? Are you unaware of churches excommunicating Galileo for daring to scholarly research into the earth rotating around the sun? Are you unaware of our own Alan Turing, of the Turing award, literally castrated not for his research but because he was a known gay researcher? Are you unaware of why HBSUs exist(black scholars were segregated for being black, their research dismissed because of the race of the researcher)?
> Are you genuinely serious or were you completely unaware of anti-communist government sanctioned blacklisting of academics suspected of being communist for political clout? Are you unaware of churches excommunicating Galileo for daring to scholarly research into the earth rotating around the sun? Are you unaware of our own Alan Turing, of the Turing award, literally castrated not for his research but because he was a known gay researcher? Are you unaware of why HBSUs exist(black scholars were segregated for being black, their research dismissed because of the race of the researcher)?
Every single one of those was NOT a mob.
The people in authority, using their authority to push their PoV, is very different to people with no authority forming a mob and demanding that the current authority silence other people from speaking their minds is a very different thing.
Whatever your view of the current authority is, it is infinitely better than mob-justice.
If you think castrating gay men wasn’t mob Justice at the time maybe you should reconsider. Same for whether or not black scholars were considered equal to white ones.
>Are you unaware of churches excommunicating Galileo for daring to scholarly research into the earth rotating around the sun?
Your point is good but this is a pet peeve of mine. Galileo was not punished by the church for saying the earth orbits the sun. Galileo was indicted and punished by the church because he was a local elite with several personal and political enemies within the church, and more directly, because he slighted the pope, his former friend, by taking a philosophical argument made by said pope, and putting it, paraphrased, into the mouth of a character in his book who was named "simplicio" and cast as a moron. That pope literally gave him permission to publish his claim that the earth orbited the sun, a claim which Galileo did not make based on science, but instead made because he felt the resulting (incorrect and based on outdated observations) mathematical model for the orbits of planets was more "elegant".
If the church truly wanted to punish him, they would not have sentenced him to literally stay at home in a beautiful villa and write books all day. His official charge was that lay people are not allowed to interpret the scripture, which he did a bit in his book. The church did not care if you made mathematical or scientific arguments about how the world worked. They only cared that you leave theology to the priests.
"Galileo was not punished by the church for saying the earth orbits the sun. Galileo was indicted and punished by the church because he was a local elite with several personal and political enemies within the church, and more directly, because he slighted the pope, his former friend, by taking a philosophical argument made by said pope, and putting it, paraphrased, into the mouth of a character in his book who was named "simplicio" and cast as a moron."
This is not really true. While there is some truth to the claim that Galileo placed an argument made by Urban VIII in the mouth of Simplicio and that Urban took offense at this, the trial documents, especially Galileo's sentence, make it very clear that Galileo was being punished for heresy and the heresy he was being punished for was the notion that Sun did not move and that the Earth did. From the sentence:
> We say, pronounce, sentence, and declare that you, the abovementioned Galileo, because of the things deduced in the trial and confessed by you as above, have rendered yourself according to this Holy Office vehemently suspected of heresy, namely of having held and believed a doctrine which is false and contrary to the divine and Holy Scripture: that the sun is the center of the world and does not moved from east to west, and the earth moved and is not the center of the world, and that one may hold and defend has probable an opinion after it has been declared and defined contrary to Holy Scripture.
Note that the term "vehemently suspect" is technical term. The Roman Inquisition in the 17th century didn't generally deliver straight up and down guilty or not-guilty verdicts and rather organized convictions according to degrees of suspicion. "Vehement suspicion" indicated that there was at least some (but not much) degree of plausible deniability that Galileo didn't believe what he had written, and that was only because Galileo denied it to the court.
"That pope literally gave him permission to publish his claim that the earth orbited the sun, a claim which Galileo did not make based on science, but instead made because he felt the resulting (incorrect and based on outdated observations) mathematical model for the orbits of planets was more "elegant"."
No, Galileo was given permission to publish a book that presented a neutral comparison of the Copernican and Ptolemaic models on mathematical grounds with the intention of proving that the Church was justified in its suppression of Copernicanism. Galileo's book was not neutral--it argued heavily in favor of Copernicanism--and that's why he got in to trouble. Urban VIII had been his friend prior to this episode so it's likely that had Galileo not placed Urban's argument in Simplicio's mouth at the end that Urban would have protected him rather than punished him, but the reason that Simplicio was given that argument was because that argument was intended to be the end of the book. After four days of continuously losing the debate, Simplicio finally raises Urban's argument about the omnipotence of God and his opponents are forced to agree with him. The idea being that Galileo could stick to the letter of his remit, while still arguing what he wanted. Unfortunately he argued too well and readers realized where is real sympathies lay. Urban VIII was accused of protecting heretics (not just Galileo, but also but supporting the French against the Hapsburgs in the 30 years war,) and so he made an example of Galileo.
Galileo's arguments were not simply mathematical. In fact, if they were, he would never have been punished because it was already permissible to treat Copernicanism as a purely mathematical hypothesis; the 1616 prohibition of Copernicanism explicitly carved out that exception. But Galileo used a wide variety of arguments, including physical arguments. Galileo after all, was primarily what we would call a physicist rather than an astronomer. It was Galileo's insistence that Copernicanism ...
Yes, I've TOF's blog posts before. TOF's perspective is one that I've encounter many times before. You mostly find it in some very conservative Catholic circles.
If I was being generous, I would say that TOF was pushing back against the overwrought hagiography that often surrounds Galileo and his confrontation with the Church. It's true that the common story is an over simplified account and there are some persistent myths that have worked their way into the story over the years into in order to make Galileo look more heroic and the Church more villainous than they either actually was. The real story is a bit complicated.
But the Church really did ban heliocentrism and it really did punish Galileo for arguing for it. That's not in question. I feel that TOF's argument otherwise rests on a deliberate misrepresentation.
TOF's argument the Church was actually just smartly waiting for proof before it changed it's doctrines is a bit rich in that Galileo offered proof and was punished for it. Now, Galileo's proof was bad, but that's not why he was punished. If it was, the Inquisitors would have mentioned that and not simply accused him of heresy for arguing that the Sun stood still. Not to mention that this whole idea rests on the bizarre notion that science works best when you silence debate until proof can be provided
Ironically, that whole argument stems from a quote from Robert Bellarmine's letter to Foscarini, where he admits that Galileo has a point about not interpreting scripture in a way that is provably false. In that quote, Bellarmine isn't saying that the Church is waiting for proof, he's saying that while proof would force him to change his mind, he doesn't think such proof is possible so he may as well go ahead and ban Copenicanism anyway. It's real obvious if you read the very next sentence that TOF for some reason doesn't quote:
> I add that the one who wrote, "The sun also ariseth, and the sun goeth down, and hasteth to his place where he arose," was Solomon. who not only spoke inspired by God, but was a man above all others wise and learned in the human sciences and in the knowledge of created things; he received all this wisdom from God; therefore it is not likely that he was affirming something that was contrary to truth already demonstrated or capable of being demonstrated.
In other words, he doesn't believe that Galileo or anybody else will be able to find proof that the sun stays still because that would contradict what he already believes based on his reading of the Bible!
So no, TOF is misrepresenting the stance of the Church in 1616. A lot of people repeat this because it looks like a clever debunking of a common myth, but it's actually more of bunking in that he inserts a lot of detail in order to disguise some blatant misrepresentation.
Mobs that try to force their own views on others are common. PTAs, or strong willed individuals with kids in school, are mob experts. Good things have come out of those mobs. Amsterdams good bicycle infrastructure was built on mob rule instead of listening to "rational engineers" who wanted to build motorways through the city.
Group think exists in science too, I mean Newton calling Leibniz a copy cat was a set back and it's crazy that I still had to learn about the priority controversy almost 300 years later. We feed on unneccessary controversy.
Exactly this. It's even worse, distrust of broad scientific consensus is purposefully cultivated to further political and economic goals, and the methods to do so increasingly perfected. This damages our ability as societies to function in a healthy way. Our capacity to navigate hard problems is diminished by the ever decreasing influence of hard science on politics and policy.
So, the reasons people learn a generalized distrust of science are that often the sausage doesn't get made. Bad science is published, applauded, cited, breathlessly covered in the media and may even be replicated, yet the first time outsiders to the field actually read the paper they realize it's nonsensical. But then they realize nobody cares because careers were made through this stuff, so why would anyone inside the field want to unmake them?
The degrading trust doesn't come from bad results per se, but rather the frequent lack of any followup combined with the lack of any institutional mechanisms to detect these problems in the first place beyond peer review, which is presented as a gold standard but is in no way adequate as such.
For example, consider how programmers use peer review. We use it, and we use lots of other tools too because peer review is hardly enough on its own to ensure quality. Now imagine you stumbled into a software company that held a cast-iron policy that because patches get reviewed by coworkers you simply don't need a test suite, nor manual testing, nor a bug tracker, code comments, security processes, strong typing, etc. And their promotion process is simply to make a ranking of developers by commit count and promote the top 10% every quarter, and fire the bottom 10%. Moreover they thought you were nuts for suggesting that there was any problem with this. You'd probably want to get out of there pretty fast, but, that's pretty much how (academic) science operates. So of course this degrades trust.
Maybe I'm missing something, but 'self-correcting' doesn't necessarily mean 'immediately self-correcting'. I guess it's safe to assume, that incorrect studies are not cementing our world view and entirely stopping us from questioning studied topics again.
The way I see the self-correcting nature of science: the truthiness of our view about specific set of topics increases over time (in some approximation).
Self-correcting doesn't mean immediately self correcting, but it does imply self-correcting in a somewhat reasonable time period, and ideally not needing to self correct too often.
What's reasonable, well, probably not years or decades. Average people cannot make major errors that destroy the value of their job output and then blow it off with "well but the company self corrected eventually so please don't fire me". When they judge science, they will judge it by the standards they are themselves held to in normal jobs.
And what's too often, well, probably papers that don't replicate should be a clear minority instead of (in some fields) the majority. Recall that failure to replicate is only one of many things that can go wrong with a study. Even if the replication rate was 100% many fields would still be filled with unusable papers.
> Without becoming an expert yourself it's very difficult to know who or what to trust.
Who says there is anyone who can be trusted? People keep looking for leaders they can trust and it takes only a brief look at history to see that the search won't stop despite the jaw dropping futility of the exercise.
The important thing is to check that people have incentives to tell the truth and no conflicts of interest. I'd trust someone untrustworthy if they were making money off my well-being. The only thing to watch out for is them not being forthright about their incentives.
We shouldn't trust that skyscrapers stay up because engineers are trustworthy. They stay up because the engineer goes down with the building.
part of the issue is that producing shit research cost very little, and debunking shit research cost a lot. just pick up one of the many "x reasons why earth is not a sphere" videos, some are pretty easy to debunk, but other require to understand i.e. potential fields (if earth is round why don't train engineer take into account curvature when laying tracks and variations thereof)
Shit research can sometimes be debunked just by running the numbers given by the shit researchers. Flat earth theorists don’t often offer up actual research (and the one group I’ve seen try got negative results and concluded they messed up, not that they were wrong).
A theory ought to be able to answer questions like “Why don’t train engineers take the curvature of the Earth into account?”
The problem is when someone comes along and thinks an unanswered question (or even just someone not knowing the answer off the top of their head) proves a theory is completely false (or worse, proves their favorite theory correct). (And to even believe the Earth is flat is to be a conspiracy theorist so in this particular case no prove will ever suffice anyway.)
I agree, but I don't think this is limited to science. I think a great deal of everything is in fact trash. This is why we need education and good faith discussion.
Science itself is the best method we have for exploring and making sense of the world around us. The method is rock solid.
In between us (gen pop) and The Method are scientists, and scientists are just as fallible as any other group of people - lawyers, politicians, coders, shop assistants.
>The problem isn't really people being anti-science or pro-science. The problem is science being done poorly, whether by scientists in the credentialed sense, or amateurs.
That's a very simplistic take on it. Bad science is a necessary part of the process and dealt with accordingly by the scientific method.
The problem is that science that is bad or incomplete is being reported as fact or truth, or arguably even worse, as entertainment in order to gain an audience. This is what actually eroded the trust in science, as people kept repeating things that reinforced and further misshaped their biases.
Human nature tends to distrust stuff we don't understand. Hence, our trust in science, which in many fields is often beyond the understanding of laymen, have to have constant reinforcement. However, the goal of media, and especially social media is to increase eyeballs for their content, and the truth sells a lot less well than sensationalised content pandering to the audience.
Simply put, there is not much profit in reporting science truthfully, and every incentive to sensationalise it.
One way that journals could actually add value (a concept to which they seem resistant) would be to review the statistical analysis. Statistics is hard and easy to get subtley wrong, and is often an independent skill to the underlying science. If journals had statistics experts to critique the analysis techniques prior to publishing it would be a great improvement in the confidence in which we could read papers.
I think this is a great idea, but in my experience there are surprisingly few such experts available. In practice, most statisticians not doing active methods research (I'm thinking of 'trial statisticians' mostly here, in CTU's) just cargo cult whatever procedures previous trials used. I guess they would pick up issues around sample size, but without also integrating that with some substantive knowledge about plausible effect sizes I'm not sure what value they would have.
Plus, it would reduce the number of publishable papers quite substantially including from high profile authors/groups, so I don't think they want the fight. We should also remember that most journal editors are also involved in publishing this research — they often have no real incentives to make things awkward.
In other words, emphasize the process more than the outcomes. If the scientific process used proves sound, then I have more confidence in the outcomes.
Alas, that's a hard sell to laypersons thru the mediums of soundbites and tweets.
I think what contributes to this phenomenon are both second-option bias[1] and motivated reasoning, at least with respect to those who choose to believe in the poor analyses.
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.
That happens when science is politicized, and any scientists critical of the “official” results is destroyed. From climate to Covid, so many areas where that happens.
No, I mean researchers and professors suddenly not getting any research grants anymore, suddenly getting fired from their tenured jobs, not being invited anymore to conferences, etc.
You could build your whole career from the 50's on discrediting the link between smoking and cancer.
The Koch brothers were (and the current brother and estate of the other) are happy to write economists checks to prove laissez-faire capitalism under a libertarian government is the best system. Or that climate change isn't real.
If you're willing to generate evidence climate change isn't real, Exxon etc have some nice checks for you.
If you're willing to show how corn syrup is good for American than the Iowa farmer association has money for you.
> The anti-intellectual, anti-scientific streak in many poor analyses claiming to debunk some scientific research is deeply concerning in our society.
People endlessly reference the Dunning-Kruger effect as a meme, without ever having read the paper, let alone having checked its methods. You don't seem to have a problem with that.
On the other hand, after seeing an article that uses essentially statistical arguments to debate a scientific study you conclude that there is some "anti-intellectual, anti-scientific streak" in our society and that it should be of grave concern.
This doesn't make any sense except as an extreme case of virtue-signaling.
Seems quite reasonable to argue that superficially plausible "debunkings" by people that apparently misunderstood a paper are more harmful to scientific progress than people casually referencing the scientist's names as a meme or insult. (And I say that as someone who didn't think the DK "debunking" argument was totally without merit)
What's more harmful to medicine: a fashionably non-expert contrarian who doesn't understand the appropriate null hypothesis making a superficially plausible statistical argument that actually the trials suggest the drug is harmful to wide acclaim from laymen, or people casually referencing or even being administered the drug without reading the original trial writeups for themselves?
I read a lot of papers on behavioural economics and psychological decision making experiments for university, like dunning-kruger, kahneman, etc and in my opinion the first autocorrelation article reads like a rebuttal paper but more informal, the approach is scientific even if it may be flawed. This is how knowledge advances. I disagree that it is anti-science. Challenging accepted postulations is good. Even famous professors make mistakes, I don't blame the writer for making an honest mistake. That's how we got this new piece of writing
Behavioural science is a pretty new field, its pretty easy to get abberant results or manipulate the results to show 'something' statistically. Many findings in earlier papers could not be replicated, or had applied statistics incorrectly, or showed different results when research participants were not white college kids.
This is a whole other problem within academia, the pressure to publish something even when there is nothing and perceived legitimacy based on the number of citations a paper has. My professor always said don't look at the number of citations, understand the method and the rebuttal, there were numerous low citation but solid papers showing flaws in famous ones but everyone who isn't deep into the subject holds the original assertion to be legitimate because its "famous"
> That said, I give the benefit of doubt to the author of "The DK Effect is Autocorrelation." It is a human error to be overly zealous in some opinions without thinking it through.
If only there were a term for "a cognitive bias whereby people with limited knowledge or competence in a given intellectual or social domain 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"
I’m not a statistician but I do have some basic training in psychometrics. It might be interesting/helpful to point out that your priors about self-assessment seem more reasonable generally but also put a lot of faith in the test’s validity as a measure of skill.
I’m relying on intuition here, but it seems a little problematic that the actual score and the predicted score are both bound to the same measurement scheme. Given that constraint on some level we’re not really talking about an external construct of skill, just test performance and whether people estimate it well. Which is different from estimating their skill well.
Maybe someone with more actual skill can elaborate or correct haha.
I read about DK and I was absolutely convinced that the effect was real. Then I read the article about DK being mere autocorrelation and I came away absolutely convinced that DK was bullshit. Then I read this article and I'm absolutely convinced that the 'DK is autocorrelation' hypothesis is utter BS. Sigh. There are lies, damned lies and statistics... :-)
They immediately reached a conclusion about something upon reading about it and now as they learned more, they understand that there might be more nuance to it
In the sense that people shouldn't let themselves be convinced by arguments they don't fully understand, that seems somewhat related to DK, in that people shouldn't be believing they are more competent than they are.
(That's not to criticize OP - when someone makes an argument that sounds convincing, it can be pretty convincing! It's just different than actually being valid.)
Consider taking a more Bayesian view of the world, especially with scientific papers. I informally tell the students I work with to look for a constellation of papers that offer supporting evidence from multiple perspectives.
Me too. I believe the effect contains a logical recursion that is impossible to escape from. Maybe the randomness variable in it? It looks as if all validations and refutations of it are always going to appear logical. I don't know what to call it or compare it with but it feels this must be documented as being a prime example of its category.
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[ 3.1 ms ] story [ 330 ms ] threadWhen you learn something you also learn what are some of the mistakes you can make. You evaluate your performance then against the mistakes you didn't make. Consider a piano player, or figure-skater. You have to know about what figures are difficult to perform to evaluate a performance, and you don't know what the difficult ones are until you have studied and tried to perform them.
If you had never listened to a professional play piano before then you'd have no idea what level of performance is possible. Similarly, if you had never seen skilled skaters perform on TV.
But we have done these things, so it's obvious that they're doing something that's very difficult.
Maybe you don't fully appreciate the skill, though. You wouldn't do well as a judge who compares the performances of professionals. But comparing novices to professionals seems easy?
"And maybe there’s no contradiction - there’s always room for nuance, for finding out where the Dunning-Kruger effect is relevant and where it’s not. That can be done with more studies, but only if the authors manage to agree on assumptions and basic statistical practice."
But we have done these things, so it's obvious that they're doing something that's very difficult.
Sometimes the things we find most impressive, in a demonstration of a skill we don't have, aren't the most difficult things.
I remember being absolutely blown away by some aerial circus tricks and stunts I saw at shows. Later, I started studying and eventually performing myself, and it's often the case that the most crowd-pleasing stunts are some of the easiest to perform.
As a performer, you could always tell which members of the audience knew their stuff, because they'd be the only ones applauding the tricks that might not have looked so spectacular, but were actually the most difficult.
Taking the piano example: after 1-2 years of progressive learning you can certainly give off the impression to somebody unfamiliar/untrained (including yourself to an extent) that you are actually quite good: Intermediate stage. But after awhile when confronted with more and more challenging stuff, by discovering different styles and finetuning your hearing; you at some point reach the very visceral and uncanny sensation of the countless possible roads you can now explore: advanced stage.
then, as a person who has lived in the world and has the normal physical skills of such you probably think "whoa, how in the heck did they do that" when you finally see it.
I linked to the start of the video where he begins to build the idea. TLDR is he mentions Monet painting Impression Sunrise and how it was something that people have never seen before and it took a bit of time for it to blow people away--they needed to develop "new eyes" to see the genius. Adam then dives into this idea of "new eyes". I'm sure many of us have experienced this in our life and it was so nice to hear Adam unpack it.
https://www.youtube.com/watch?v=qE7dYhpI_bI&t=122s
> After two months in the bakery, you learned how to “see” clean.
> Code is the same way.
[1] https://www.joelonsoftware.com/2005/05/11/making-wrong-code-...
If human cultures can be characterized as default arrogant or default humble then it stands to reason that arrogant cultures will have a DK effect, and in humble cultures you won't.
There is also the other end of the scale where "skilled and unaware" occurs: people under-assessing their skill (presumed that this is due to judging that most people also have similarly high skill levels).
I think your two "cultures" would shift the self-assessment line up or down on the graph (constant), but not affect the slope very much (multiplier). The line shape or line slope must change somewhat since values are limited (between 0 and 100).
When I did some cognitive behaviour therapy, I un-learned things like "all or nothing thinking" and the expectation that I could accurately predict the outcome of any course of action by modeling future performance off of a past failure.
Do you know any words, stereotypes, or clichés for this? Or even what the related mental disorder is called if it were to become debilitating? Or a specific word for the complete clustering of related signals?
I am guessing those issues plus there related issues (¿syndromic?) are common - but I don’t know where to group it in my own mind.
It is closely associated with having a highly systematizing mind. People with ASD get drawn and pushed down a particular life history corridor. There are rewards of parental/teacher approval for high-performance in an area of profound interest, and a punishment in thef orm of peer bullying for low social skills. This conditions them to operate this way to avoid bullying and optimize for time seemingly well-spent with these impersonal systems. To justify one's own existence, there is this urge to live in a world where a narrowly focused mind is able to predictably produce an ideal world through expertise (all), and a tendency towards refusal to live outside of that, sometimes advancing into self destructive behaviour should that not be an available outcome (nothing). Getting ALL is not only about having things one wants, it is also about seeing the system work, and about identity, a sense of vindication.
Collective layer:
We see so much of it I think even among neurotypicals because we live in an extremely systematized world. Every single aspect of our world is seen by the "haves" of our society as a candidate for profitable separation through a digital layer. Anyone can end up metastasizing a systematizing mind. They just need to exhibit a hyper-focus on something impersonal and complex. As a global civilization, we have been doing this to ourselves and strapping others into it as much as we can. Mostly, only those living in rural parts of materially impoverished nations are spared this temptation.
Mental Symbolism layer:
It is symbolically speaking one entrance into a realm of mental death. With the devotion to lifeless systems the human is de-personalized, atomized, depressed, unrelational. They leave unfulfilled the inescapable truth of what it means to be human. To live your entire life this way is to betray your parents, ancestors, any friends or lovers you ever had, and anyone you could have helped.
Spiritual layer:
The pattern is quite literally satanic. The person has chosen to reign in this hell (both all and nothing), rather than serve in heaven (the humble, narrow middle path). The most powerful and beautiful of all created entities, with an astonishingly powerful mind, insisting on dwelling in a state of supreme perfection betrays the Father to become an engine of extinction.
There are people who can't advance because they can't see the problem, and people who can't advance because they can't (or don't want to) correct it. The end result is the same though.
Conversely it cannot also be under zero, so error is most likely going to be above the actual skill line (over estimation, since it's clamped below it).
It's not that at all. The assumption should be that everyone is equally good (or bad) at assessing their performance. Not that they have no ability but that the means between groups is the same vs. not the same. That the ability to assess themselves is independent of performance.
Say that everyone is equally okay at assessing themselves, and get within 0.1 of their actual performance (rated from 0 to 1). Then X and Y are going to be very correlated, as X - 0.1 < Y < X + 0.1. But X-Y will look like a random plot, since Y is randomly sampled around X.
The only case where X and Y wouldn't correlate at all is if people have no ability to assess their performance (IE, Y isn't sampled around X, but is instead sampled from a fixed range).
I honestly think people take it way too seriously and apply it too generally. Quantifying "good" is hard if you don't know much about the field you're quantifying. Getting deep into a particular field is humbling -- Tetris seems relatively simple, but there are people who could fill a book with things _I_ don't know about it, despite playing at least a few hundred hours of it.
Is there an answer to that humility gained by being an expert in one field being translated to better self-assessment in other fields? I feel myself further appreciating the depth and complexity of fields I "wrote off" as trivial and uninteresting when I was younger as I get deeper into my own field (and see just how much deeper it is too).
I think that often the opposite is true: people who become experts in one domain often assume that they are automatically experts in completely unrelated fields. I suspect that this is the cause of "Nobel disease": https://en.wikipedia.org/wiki/Nobel_disease
"there's nothing more annoying than a physicist encountering a new subject"
Imagine in the Dunning-Kruger chart the second plot (perceived ability) was a horizontal line at 70, which is not true but not far off from the real results. Now imagine I told you "did you know that, regardless of their actual score, everyone thought they got a 70?" That's a surprising fact.
There's really no contradiction there; all it takes is for there to be a couple low scores pulling the average down.
The arithmetic mean and the median are both averages, but the upthread comment was about the median and yours about the arithmetic mean.
> There's really no contradiction there; all it takes is for there to be a couple low scores pulling the average down.
Well, no, when what you are estimating is relative performance by score percentiles, and people's self evaluation is biased toward the 70th percentile, that's not what is happening.
Edit-see below I meant opposite not corollary.
The less you know, the more random your guess at your own knowledge is. The actual value is low and less than zero isn't an option, so this drags the average up consistently.
The more you know, the more accurate your guess of your knowledge is. Especially as you hit the limits of the test, this noise can only drag the average down, but less dramatically than the other case.
With the reasonable conclusion: We all suck at guessing how much we know, but the more you know the less you suck until you hit the limits of the framework you are using for quantization of knowledge.
There are ways to fix this:
- Throw out the extreme high and low ends of the data bc the model breaks down there. (Which results in a very boring result)
- Have people guess their score and a rough level of confidence along side it (just a 0-5 sort of thing) and see what happens.
Note that I actually do think from my own experience that the effect is real, but the arguments presented fail to prove it statistically bc the model breaks down at the extreme where the effect is detected.
Most comments were splitting hairs on what _exactly_ the Dunning-Kruger effect was, plus some general nerd-sniping on how the original article was off base.
IMO it was something that fell flat on its own rather than something that needed a lengthy refutation, but I can understand that sometimes these things get under your skin.
The author says as much in this article:
> Why so angry? [...] [Frankly], for the last few years, the world seems to be accelerating the rate at which it’s going crazy, and it feels to me a lot of that is related to people’s distrust in science (and statistics in particular). Something about the way the author conveniently swapped “purely random” with “null hypothesis” (when it’s inappropriate!) and happily went on to call the authors “unskilled and unaware of it”, and about the ease with which people jumped on to the “lies, damned lies, statistics” wagon but were very stubborn about getting off, got to me. Deeply. I couldn’t let this go.
It's true, the previous article (https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...) was pretty harsh on the authors of the original paper:
> In their seminal paper, Dunning and Kruger are the ones broadcasting their (statistical) incompetence by conflating autocorrelation for a psychological effect. In this light, the paper’s title may still be appropriate. It’s just that it was the authors (not the test subjects) who were ‘unskilled and unaware of it’.
But on some level, the original paper sounds just as condescending and dismissive. It presents a scholarly and statistical framework for looking down on "the incompetent" (a phrase used four times in the original paper). In practice, most of the times I see the DK effect cited, it functions as a highbrow and socially acceptable way of calling someone else stupid, in not so many words.
Cards on the table, I've never liked DK discourse for this reason. It's always easy to imagine others as the "Unskilled and Unaware", and for this reason bringing DK into any discussion rarely generates much insight.
I think it's even worse that that: it's also a socially acceptable way of enforcing credentialism and looking down on others for not having a sufficiently elite education.
I find the use of quartiles suspicious, personally. It's very nearly the ecological fallacy[1].
> I’m not going to start reviewing and comparing signal-to-noise ratios in Dunning-Kruger replications
DK has been under fire for a while now, nearly as long as the paper has existed[2]. At present, I am in the "effect may be real but is not well supported by the original paper" camp. If DK wanted to they could release the original data, or otherwise encourage a replication.
[1]: https://en.wikipedia.org/wiki/Ecological_correlation [2]: https://replicationindex.com/2020/09/13/the-dunning-kruger-e...
If others can't replicate it entirely on their own without "encouragement". Then it isn't useful at all and the original experiment can be safely ignored as irrelevant to humanity, along with any "prestige" associated with it.
It's definitely related to ecological fallacy in the sense that both underestimate relative error and inflate effect sizes.
1. Average self assessment coincides with true skill, but variance increases with low skill.
2. Average self assessment is biased, and the bias is positive when you are unskilled and negative when you're highly skilled.
These two situations would create indistinguishable DK-graphs. I don't understand how anyone can be sure on either (1) or (2) after seeing one instance of such a graph.
As I see it, the only way out for "DK positivists" is to say that the DK hypothesis is unrelated to the truth values of (1) and (2). Or, that there is other evidence making DK convincing.
Neither seems very plausible!
It serves mostly as a way of reassuring themselves of their own superiority. The message (for them) basically amounts to "other people's claim to knowledge is just further proof that they don't know anything."
I feel like I’m honestly yet to see somebody make DK accusations in a way that’s not totally cringe.
I recall that either Dunning or Kruger once made a remark to that effect. That rather than an indictment of stupid people, it would be better to view it as a warning to those who consider themselves the smart ones.
Now with D-K the proposed problem is statistical autocorrelation, not systematic bias, due to lack of independence, as here:
> "Subtracting y – x seems fine, until we realize that we’re supposed to interpret this difference as a function of the horizontal axis. But the horizontal axis plots test score x. So we are (implicitly) asked to compare y – x to x"
Regardless, it's fairly obvious that D-K enthusiasts are of the opinion that a small group of expert technocrats should be trusted with all the important decisions, as the bulk of humanity doesn't know what's good for it. This is a fairly paternalistic and condescending notion (rather on full display during the Covid pandemic as well). Backing up this opinion with 'scientific studies' is the name of the game, right?
It does vaguely remind me of the whole Bell Curve controversy of years past... in that case, systematic bias was more of an issue:
> "The last time I checked, both the Protestants and the Catholics in Northern Ireland were white. And yet the Catholics, with their legacy of discrimination, grade out about 15 points lower on I.Q. tests. There are many similar examples."
https://www.nytimes.com/1994/10/26/opinion/in-america-throwi...
I am reminded of something my very accomplished PI (in the field of earth system science) confided privately to me once... "Purely statistical arguments," she said, "are mostly bullshit..."
It seems like you're roughly the only person who thinks this.
And that's the point the original article was trying to make ("The reason turns out to be embarrassingly simple: the Dunning-Kruger effect has nothing to do with human psychology. It is a statistical artifact — a stunning example of autocorrelation."), though that point does lost a bit as it goes on.
I think this article gives a better summary of how the Dunning-Kruger effect probably isn't a psychological effect: https://www.mcgill.ca/oss/article/critical-thinking/dunning-...
This isn't true either. Statistical dependence does not determine or uniquely identify causal interpretation or system structure. See Judea Pearl's works (e.g. The Book of Why) for more on this.
People lacking the ability to self-assess is interesting psychologically. People can learn from experience in many other contexts. People can judge their relative position versus other people in many contexts. Why would they be so bad at this particular task? There could be a psychological underpinning.
Even if it turns out we have useless noise-emitting fluff in the place that would produce self-awareness of skill, that would be a psychological cause of a psychological effect. Not the ones that Dunning and Kruger believed they were seeing, but still.
Now, if you asked frogs for a self-assessment of skill, I would expect that data would not show any psychological effects.
It makes sense as a robotically developed null hypothesis, but it doesn't make sense in the real world.
Is there an existing and relevant phenomenon about people lacking the ability to self-asses, that is true, proven, and not just trivia?
I do believe that people understand all the available information about their skills and performance, and they rate themselves according to it.
E.g. if they are asked about whether they perform good on an IQ test against their classmates they will produce noise (see E.g. the article "I can't let go of.."), and if they have the results of the IQ test, they will be able correctly calculate in which quartile they are.
Is there anything against this view?
However, by "random data", the original blog means people and their self-assessments are completely independent! In fact, this is exactly what the DK effect is saying -- people are bad at self-evaluating [2]. (More precisely, poor performers overestimate their ability and high performers underestimate their ability.) In other words, the premise of the original blog post [1] is exactly the conclusion of DK!
Looking at the HN comments cited [3] by the current blog post, it appears that the main point of contention from other commenters was whether the DK effect means uncorrelated self-assessment or inversely correlated self-assessment. The DK data only supports the former, not the latter. I haven't looked at the original paper, but according to Wikipedia [2], the only claim being made appears to be the "uncorrelated" claim. (In fact, it is even weaker, since there is a slight positive correlation between performance and self-assessment.)
So, my conclusion would be that DK holds, but it does depend on exactly what is the exact claim in the original DK paper.
[1] https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...
[2] https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect
[3] https://news.ycombinator.com/item?id=31036800
Is it that hard to actually check the original paper before bothering to make such a claim? The original paper explicitly claims to examine "why people tend to hold overly optimistic and miscalibrated views about themselves".
Their claim that "If we have been working with random numbers, how could we possibly have replicated the Dunning-Kruger effect?" is the first blatantly false statement, and then the rest is built upon that so it can be safely disregarded.
It's easy to see this because while the effect is present if everyone evaluates themselves randomly, it's not present if everyone accurately evaluates themselves, and these are both clearly possible states of the world a priori, so it's a testable hypothesis about the real world, contrary to the bizarre claim in the paper.
Also, the knowledge that the authors published that article provides evidence for the Dunning-Kruger effect being stronger than one would otherwise believe.
Like similar analyses here you don't factor in that DK is about bias. Of course you can't see bias when test score=self assessment. That's because "IF everyone perfectly knows their score then there is no bias in their assessment" is a tautology.
Daniel: >It’s not a “statistical artifact” - that will be your everyday experience living in such a world.
You can experience statistical effects. I think a lot of controversy comes from how Dunning and Kruger's paper leads people to interpret the data as hubris on the part of low-performers, and the statistical analysis demolishes that interpretation. Not knowing how well you performed is not the same thing psychologically as "overestimating" your performance.
Dunning Krueger is precisely about the surprising result that people are bad at estimating their performance!
If you accept the 'D-K is autocorrelation' argument, you don't get to throw out the existence of the D-K effect: you are saying Dunning + Krueger failed to show that humans have any ability to estimate how skilled they are at all.
That seems like an even more radical position than the D-K thesis.
Isn't DK about estimating your performance relative to the rest of the population? To do that, you need to not only know your own performance but also everyone else's. To me, guessing the performance of others sounds quite difficult.
Of course, there are tests where the bottom quartile find the majority of it easy and have no particular reason to assume that most others found it even easier, and circumstances in which the weak undergrad who can only answer half the questions may reasonably believe that the test is being administered to a general population full of people who won't understand any of the material at all. But in general, it's reasonable to assume that if there's a lot of stuff you don't know, other people will know better.
You are sort of smuggling in the assumption for example that Olympian medalist lifters, when asked how much they can deadlift, will have the same distribution of answers as people who never deadlift (but are aware that totally sedentary men can probably deadlift like 200lbs and totally sedentary women can probably deadlift like 150lbs). If this were true, it would be worth publishing a paper about it.
It's sort of surprising to me to read your comment because TFA is an extended rebuttal of your comment.
> I think a lot of controversy comes from how Dunning and Kruger's paper leads people to interpret the data as hubris on the part of low-performers, and the statistical analysis demolishes that interpretation. Not knowing how well you performed is not the same thing psychologically as "overestimating" your performance.
D-K actually found that low performers were less accurate at assessing their skill than high performers, and the article you refer to obviously did not find this effect in random data, so I'm not sure how it was demolished.
So it seems people are bad at doing global rankings. If I tried to rank myself amongst all programmers worldwide, that seems really hard and I could see myself picking some "safe" above-average value just because I don't know that many other people.
There's also: If you take 1 class in piano 30 years ago and can only play 1 simple song, that might put you in the 90th percentile worldwide just because most people can't play at all. But you might be at the 10th percentile amongst people who've taken at least 1 class. So doing a global ranking can be very difficult if you aren't exactly sure what the denominator set looks like.
So I think it's an artifact of using "ranking" as an axis. If the metric was, "predict the percentage of questions you got correct" vs. "predict your ranking", maybe people would be more accurate because it wouldn't involve estimating the denominator set.
Yes, and this literally implies that people in the lowest quartiles can't and won't rate themselves to be in the lowest quartiles when they are forced to give an answer. (Especially on tests that doesn't measure anything (getting jokes? really?), on tests that they have no knowledge about (how would they know that how their classmates perform on an IQ test???), or on tests that just have a high variance.)
And therefore they will "overestimate their performance".
It's like grouping a bunch of random people, and forcing them to answer whether their house is short, average or high. The "people living in short houses" will "overestimate the height of their houses", while the "people living in towers" will humbly say they live in an average high house.
Is this an existing and relevant psychological phenomenon, different from the general inability to guess unknown things? I don't think so.
If you think so, then give me proof.
> how would they know that how their classmates perform on an IQ test???
Are you serious? If you're interacting with your classmates, you definitely should have some idea on how their intellectual capabilities differ between each other and also with respect to you. In a small class doing lots of things together, someone might even literally count their "ranking" at some metric that highly correlates with IQ, estimating that Bob, Jane and Mary are above me and Dan and Juliet are below me, so I'm at 40th percentile.
It's not appropriate to treat these aspects as unknown things or unknowable things.
1) self-assessment is perfectly correlated with skill, or
2) completely uncorrelated.
I think neither of these makes sense as a null hypothesis.
The model you describe matches my intuition about what we should expect: people know something about their own skill level, but not everything.
And this is a true dichotomy. The "autocorrelative" effect doesn't need perfect correlation, just some correlation.
> Why so angry? I know I’ve taken this far too personally. I have no illusions that everything I read online should be correct, or about people’s susceptibility to a strong rhetoric cleverly bashing conventional science, even in great communities such as HN. But frankly, for the last few years, the world seems to be accelerating the rate at which it’s going crazy, and it feels to me a lot of that is related to people’s distrust in science (and statistics in particular). Something about the way the author conveniently swapped “purely random” with “null hypothesis” (when it’s inappropriate!) and happily went on to call the authors “unskilled and unaware of it”, and about the ease with which people jumped on to the “lies, damned lies, statistics” wagon but were very stubborn about getting off, got to me. Deeply. I couldn’t let this go.
I am afraid I actually agree with the author's point. The anti-intellectual, anti-scientific streak in many poor analyses claiming to debunk some scientific research is deeply concerning in our society. If someone is trying to debunk some scientific research, at least he should learn some basic analytic tools. This observation is independent of whether the original DK paper could have been better.
That said, I give the benefit of doubt to the author of "The DK Effect is Autocorrelation." It is a human error to be overly zealous in some opinions without thinking it through.
I recall one study that said all white people are committing environmental racism against all non-white people. I dove in and read the whole thing wondering what method could have yielded scientific confidence in such a broad result. Turns out the model used was a semi-black box that required a request for access and a supercomputer to run. But it was in a Peer Reviewed Scientific Journal and had lots of Graduate Level Statistics so I guess it seemed trustworthy.
I don't think this is true. It is possible to put a lot of work into unsound statistics and to make a lot of "noise and fury" about how mathematical you are while failing some basic principle, but I don't think sound statistics can mislead. The replication crisis was caused by scientists not being rigorous and journals not forcing them to be. You absolutely cannot accept publication as a sign of sound techniques except in journal/field combinations that have a deserved reputation.
Of course they can, unless you magically exclude all statistics that made a bad assumption on independence.
I plot all the daily high temperatures and the presence of the ice cream cart and it turns out the ice cream cart causes warmer highs! Solid statistics.
Turns out the guy that has the ice cream cart has a weather app on his phone though and doesn’t come out on forecasted cold days.
It is literally like this:
- someone makes a point that questions your believe
- you google a phrase that would come in studies that proof otherwise
- you take the first thing that looks promising, and fly over the first page, and paraphrase a good bit in a way that makes your point
- you publish it as part of a post, youtube video or whatever
- danger averted
Bad studies play into this, but even if the studies are good, or bad studies that have been retracted the same thing happens. James Wakefield who originally published the "combined vaccines cause autism study" after patenting a non-combined measles vaccine had his study retracted by the lancet soon after publication. He lost his status as a doctor etc. And you will still find people who use his study as a source.
Of course studies whose outcome collide with our believe systems are always harder to trust than those who validate it — but this is why you look at the methods used and other indicators that might make that study bogus.
The book "How to lie with statistics" is one of the best statistics textbooks that I have read. It basically makes you immune to misleading stats (charts, tables, everything).
IIRC, the only thing that is missing from the book (it's a really old book) which is very relevant is p-hacking.
There is no pat "trust science more" or "trust amateurs less" answer here. The actual answer is that if you want to understand research, you need to actually understand mathematical statistics and the philosophy of statistics fairly deeply. There just isn't any way around it.
1. https://journals.plos.org/plosmedicine/article?id=10.1371/jo...
On the other end there's distrust of broad scientific consensus across different professions, countries, etc. It's the distrust at these levels that is the increasing problem we are facing today.
A problem here is that there are fields of science that are almost certainly bogus in themselves. One very likely candidate is nutrition, which seems to be fumbling in the dark and has a long history of producing worse recommendations than doing nothing (e.g. replace fat with sugar). More controversially, the entire field of economics is seen by some to be very suspect from a basic foundations view.
It's not the field that is bogus in that case. People were quite literally bribed to push this. This could happen anywhere, anytime, in any field.
Basically the digestive system is far too complex for us to understand from first principles at this time. Your diet has a very complex and very slow effect on your body, with some exceptions. Numerous diseases and environmental factors impact how this plays out exactly. So, to do real research in nutrition, the only chance right now would be to conduct massive studies over long periods of time with rigorous controls on subjects' nutrition and activities - which is basically impossible, or at least prohibitively expensive.
Instead, we get conclusions drawn from studies of a few dozen people over a few months or at best a year (in "long-term" studies). Or, we get conclusions drawn from comparing diet across huge populations ("the Mediterranean diet", "the Japanese diet", "the American diet" etc) with no possibility to control for obvious differences in nutrients, environmental factors, lifestyle differences, access to healthcare etc. Both of these are worthless conclusions, they don't tell you anything at all.
The only successes of nutrition science have been identifying the most basic nutrients we need to survive at a basic level (protein, fat, carbohydrates, and the various vitamins and minerals). Basically nothing beyond that should be trusted.
As a fun historical note, after the discovery of the macro-nutrients there was a budding field of nutrition scientists confidently recommending optimal diets using scientific methods. Unfortunately, they had no idea about the existence of micro-nutrients, so actually following some of their diets you could actually end up getting scurvy or other serious malnutrition diseases. The current slew is not that bad, but I wouldn't be surprised if in the future we will look back similarly at some common diet advice of today.
It's more than that, I think. Sibling-thread poster hit the nail on the head when he complained of politicised science.
The social sciences have this dominating and silencing effect on the rest of the sciences.
There's always been junk science, and when found out it gets discredited. This is still happening and is a good thing.
What's new is that any research that might produce results counter to the what the PC-mob deems acceptable is attacked. Whether or not there is consensus amongst researchers in that field is irrelevant when the mob calls for the firing of any researcher who doesn't toe the current political party-line.
Sure, we're not actually in the dark ages, but a trend of silencing voices in the name of purity of thought is particularly troubling, especially as the mob asking for this is unashamedly attempting to implement NewsSpeak[1].
[1] See the argument in yesterdays threads about what "man" and "woman" mean, and should dictionaries be changed, etc.
> No this is not new.
I don't recall a PC-mob being used to silence any and all non-supportive voices until quite recently.
> You have always have a direction set by political views, even if we have decided they are wrong they are still hard to kill like: smoking is good, white people are superior. There is still "science" being done to bolster those political views.
I don't see what that has to do with that I said - that a very vocal bunch of non-science people seem to have successfully lobbied into silencing specific topics.
Are you genuinely serious or were you completely unaware of anti-communist government sanctioned blacklisting of academics suspected of being communist for political clout? Are you unaware of churches excommunicating Galileo for daring to scholarly research into the earth rotating around the sun? Are you unaware of our own Alan Turing, of the Turing award, literally castrated not for his research but because he was a known gay researcher? Are you unaware of why HBSUs exist(black scholars were segregated for being black, their research dismissed because of the race of the researcher)?
Politics in academia isn’t new, like, at all.
Every single one of those was NOT a mob.
The people in authority, using their authority to push their PoV, is very different to people with no authority forming a mob and demanding that the current authority silence other people from speaking their minds is a very different thing.
Whatever your view of the current authority is, it is infinitely better than mob-justice.
Your point is good but this is a pet peeve of mine. Galileo was not punished by the church for saying the earth orbits the sun. Galileo was indicted and punished by the church because he was a local elite with several personal and political enemies within the church, and more directly, because he slighted the pope, his former friend, by taking a philosophical argument made by said pope, and putting it, paraphrased, into the mouth of a character in his book who was named "simplicio" and cast as a moron. That pope literally gave him permission to publish his claim that the earth orbited the sun, a claim which Galileo did not make based on science, but instead made because he felt the resulting (incorrect and based on outdated observations) mathematical model for the orbits of planets was more "elegant".
If the church truly wanted to punish him, they would not have sentenced him to literally stay at home in a beautiful villa and write books all day. His official charge was that lay people are not allowed to interpret the scripture, which he did a bit in his book. The church did not care if you made mathematical or scientific arguments about how the world worked. They only cared that you leave theology to the priests.
This is not really true. While there is some truth to the claim that Galileo placed an argument made by Urban VIII in the mouth of Simplicio and that Urban took offense at this, the trial documents, especially Galileo's sentence, make it very clear that Galileo was being punished for heresy and the heresy he was being punished for was the notion that Sun did not move and that the Earth did. From the sentence:
> We say, pronounce, sentence, and declare that you, the abovementioned Galileo, because of the things deduced in the trial and confessed by you as above, have rendered yourself according to this Holy Office vehemently suspected of heresy, namely of having held and believed a doctrine which is false and contrary to the divine and Holy Scripture: that the sun is the center of the world and does not moved from east to west, and the earth moved and is not the center of the world, and that one may hold and defend has probable an opinion after it has been declared and defined contrary to Holy Scripture.
Note that the term "vehemently suspect" is technical term. The Roman Inquisition in the 17th century didn't generally deliver straight up and down guilty or not-guilty verdicts and rather organized convictions according to degrees of suspicion. "Vehement suspicion" indicated that there was at least some (but not much) degree of plausible deniability that Galileo didn't believe what he had written, and that was only because Galileo denied it to the court.
"That pope literally gave him permission to publish his claim that the earth orbited the sun, a claim which Galileo did not make based on science, but instead made because he felt the resulting (incorrect and based on outdated observations) mathematical model for the orbits of planets was more "elegant"."
No, Galileo was given permission to publish a book that presented a neutral comparison of the Copernican and Ptolemaic models on mathematical grounds with the intention of proving that the Church was justified in its suppression of Copernicanism. Galileo's book was not neutral--it argued heavily in favor of Copernicanism--and that's why he got in to trouble. Urban VIII had been his friend prior to this episode so it's likely that had Galileo not placed Urban's argument in Simplicio's mouth at the end that Urban would have protected him rather than punished him, but the reason that Simplicio was given that argument was because that argument was intended to be the end of the book. After four days of continuously losing the debate, Simplicio finally raises Urban's argument about the omnipotence of God and his opponents are forced to agree with him. The idea being that Galileo could stick to the letter of his remit, while still arguing what he wanted. Unfortunately he argued too well and readers realized where is real sympathies lay. Urban VIII was accused of protecting heretics (not just Galileo, but also but supporting the French against the Hapsburgs in the 30 years war,) and so he made an example of Galileo.
Galileo's arguments were not simply mathematical. In fact, if they were, he would never have been punished because it was already permissible to treat Copernicanism as a purely mathematical hypothesis; the 1616 prohibition of Copernicanism explicitly carved out that exception. But Galileo used a wide variety of arguments, including physical arguments. Galileo after all, was primarily what we would call a physicist rather than an astronomer. It was Galileo's insistence that Copernicanism ...
Have you read this perspective before? Is it a well known "oh not this again" incorrect source in the world of renaissance italy theology and history?
If I was being generous, I would say that TOF was pushing back against the overwrought hagiography that often surrounds Galileo and his confrontation with the Church. It's true that the common story is an over simplified account and there are some persistent myths that have worked their way into the story over the years into in order to make Galileo look more heroic and the Church more villainous than they either actually was. The real story is a bit complicated.
But the Church really did ban heliocentrism and it really did punish Galileo for arguing for it. That's not in question. I feel that TOF's argument otherwise rests on a deliberate misrepresentation.
TOF's argument the Church was actually just smartly waiting for proof before it changed it's doctrines is a bit rich in that Galileo offered proof and was punished for it. Now, Galileo's proof was bad, but that's not why he was punished. If it was, the Inquisitors would have mentioned that and not simply accused him of heresy for arguing that the Sun stood still. Not to mention that this whole idea rests on the bizarre notion that science works best when you silence debate until proof can be provided
Ironically, that whole argument stems from a quote from Robert Bellarmine's letter to Foscarini, where he admits that Galileo has a point about not interpreting scripture in a way that is provably false. In that quote, Bellarmine isn't saying that the Church is waiting for proof, he's saying that while proof would force him to change his mind, he doesn't think such proof is possible so he may as well go ahead and ban Copenicanism anyway. It's real obvious if you read the very next sentence that TOF for some reason doesn't quote:
> I add that the one who wrote, "The sun also ariseth, and the sun goeth down, and hasteth to his place where he arose," was Solomon. who not only spoke inspired by God, but was a man above all others wise and learned in the human sciences and in the knowledge of created things; he received all this wisdom from God; therefore it is not likely that he was affirming something that was contrary to truth already demonstrated or capable of being demonstrated.
In other words, he doesn't believe that Galileo or anybody else will be able to find proof that the sun stays still because that would contradict what he already believes based on his reading of the Bible!
So no, TOF is misrepresenting the stance of the Church in 1616. A lot of people repeat this because it looks like a clever debunking of a common myth, but it's actually more of bunking in that he inserts a lot of detail in order to disguise some blatant misrepresentation.
Scopes would like a word with you.
Put simply, science is advertised as self-correcting but in reality it's not. Representative experience documented here: http://crystalprisonzone.blogspot.com/2021/01/i-tried-to-rep...
So, the reasons people learn a generalized distrust of science are that often the sausage doesn't get made. Bad science is published, applauded, cited, breathlessly covered in the media and may even be replicated, yet the first time outsiders to the field actually read the paper they realize it's nonsensical. But then they realize nobody cares because careers were made through this stuff, so why would anyone inside the field want to unmake them?
The degrading trust doesn't come from bad results per se, but rather the frequent lack of any followup combined with the lack of any institutional mechanisms to detect these problems in the first place beyond peer review, which is presented as a gold standard but is in no way adequate as such.
For example, consider how programmers use peer review. We use it, and we use lots of other tools too because peer review is hardly enough on its own to ensure quality. Now imagine you stumbled into a software company that held a cast-iron policy that because patches get reviewed by coworkers you simply don't need a test suite, nor manual testing, nor a bug tracker, code comments, security processes, strong typing, etc. And their promotion process is simply to make a ranking of developers by commit count and promote the top 10% every quarter, and fire the bottom 10%. Moreover they thought you were nuts for suggesting that there was any problem with this. You'd probably want to get out of there pretty fast, but, that's pretty much how (academic) science operates. So of course this degrades trust.
The way I see the self-correcting nature of science: the truthiness of our view about specific set of topics increases over time (in some approximation).
What's reasonable, well, probably not years or decades. Average people cannot make major errors that destroy the value of their job output and then blow it off with "well but the company self corrected eventually so please don't fire me". When they judge science, they will judge it by the standards they are themselves held to in normal jobs.
And what's too often, well, probably papers that don't replicate should be a clear minority instead of (in some fields) the majority. Recall that failure to replicate is only one of many things that can go wrong with a study. Even if the replication rate was 100% many fields would still be filled with unusable papers.
Who says there is anyone who can be trusted? People keep looking for leaders they can trust and it takes only a brief look at history to see that the search won't stop despite the jaw dropping futility of the exercise.
The important thing is to check that people have incentives to tell the truth and no conflicts of interest. I'd trust someone untrustworthy if they were making money off my well-being. The only thing to watch out for is them not being forthright about their incentives.
We shouldn't trust that skyscrapers stay up because engineers are trustworthy. They stay up because the engineer goes down with the building.
A theory ought to be able to answer questions like “Why don’t train engineers take the curvature of the Earth into account?”
The problem is when someone comes along and thinks an unanswered question (or even just someone not knowing the answer off the top of their head) proves a theory is completely false (or worse, proves their favorite theory correct). (And to even believe the Earth is flat is to be a conspiracy theorist so in this particular case no prove will ever suffice anyway.)
In between us (gen pop) and The Method are scientists, and scientists are just as fallible as any other group of people - lawyers, politicians, coders, shop assistants.
That's a very simplistic take on it. Bad science is a necessary part of the process and dealt with accordingly by the scientific method.
The problem is that science that is bad or incomplete is being reported as fact or truth, or arguably even worse, as entertainment in order to gain an audience. This is what actually eroded the trust in science, as people kept repeating things that reinforced and further misshaped their biases.
Human nature tends to distrust stuff we don't understand. Hence, our trust in science, which in many fields is often beyond the understanding of laymen, have to have constant reinforcement. However, the goal of media, and especially social media is to increase eyeballs for their content, and the truth sells a lot less well than sensationalised content pandering to the audience.
Simply put, there is not much profit in reporting science truthfully, and every incentive to sensationalise it.
Plus, it would reduce the number of publishable papers quite substantially including from high profile authors/groups, so I don't think they want the fight. We should also remember that most journal editors are also involved in publishing this research — they often have no real incentives to make things awkward.
Alas, that's a hard sell to laypersons thru the mediums of soundbites and tweets.
[1] https://rationalwiki.org/wiki/Essay:Second-option_bias
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.
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.
There are valid criticisms of the DK study, though. See this comment for example: https://news.ycombinator.com/item?id=31119196
> 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.
https://statmodeling.stat.columbia.edu/2017/01/07/we-fiddle-...
What billionaire funds “contrarian” research?
Think also of all the money funding "climate change isn't real" or "climate change is not anthropomorphic" which can be traced back to big oil money.
The Koch brothers were (and the current brother and estate of the other) are happy to write economists checks to prove laissez-faire capitalism under a libertarian government is the best system. Or that climate change isn't real.
If you're willing to generate evidence climate change isn't real, Exxon etc have some nice checks for you.
If you're willing to show how corn syrup is good for American than the Iowa farmer association has money for you.
> If you're willing to generate evidence climate change isn't real, Exxon etc have some nice checks for you.
Do you have any evidence of either of these being true today?
People endlessly reference the Dunning-Kruger effect as a meme, without ever having read the paper, let alone having checked its methods. You don't seem to have a problem with that.
On the other hand, after seeing an article that uses essentially statistical arguments to debate a scientific study you conclude that there is some "anti-intellectual, anti-scientific streak" in our society and that it should be of grave concern.
This doesn't make any sense except as an extreme case of virtue-signaling.
What's more harmful to medicine: a fashionably non-expert contrarian who doesn't understand the appropriate null hypothesis making a superficially plausible statistical argument that actually the trials suggest the drug is harmful to wide acclaim from laymen, or people casually referencing or even being administered the drug without reading the original trial writeups for themselves?
Behavioural science is a pretty new field, its pretty easy to get abberant results or manipulate the results to show 'something' statistically. Many findings in earlier papers could not be replicated, or had applied statistics incorrectly, or showed different results when research participants were not white college kids.
This is a whole other problem within academia, the pressure to publish something even when there is nothing and perceived legitimacy based on the number of citations a paper has. My professor always said don't look at the number of citations, understand the method and the rebuttal, there were numerous low citation but solid papers showing flaws in famous ones but everyone who isn't deep into the subject holds the original assertion to be legitimate because its "famous"
If only there were a term for "a cognitive bias whereby people with limited knowledge or competence in a given intellectual or social domain 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"
I’m not a statistician but I do have some basic training in psychometrics. It might be interesting/helpful to point out that your priors about self-assessment seem more reasonable generally but also put a lot of faith in the test’s validity as a measure of skill.
I’m relying on intuition here, but it seems a little problematic that the actual score and the predicted score are both bound to the same measurement scheme. Given that constraint on some level we’re not really talking about an external construct of skill, just test performance and whether people estimate it well. Which is different from estimating their skill well.
Maybe someone with more actual skill can elaborate or correct haha.
(That's not to criticize OP - when someone makes an argument that sounds convincing, it can be pretty convincing! It's just different than actually being valid.)