23 ms·

I can't let go of “The Dunning-Kruger Effect is Autocorrelation”

• irrational 4y ago
  > If you tell me you didn’t have a single serious thought of self-assessing today, even semi-conscious, I simply won’t believe you. I stopped reading at this point. Someone that is so certain that they say “I simply won’t believe you.” is too self-assured to be worth paying much attention to.
  • Scea91 4y ago
    If you tell me that Earth is flat I simply won't believe you.
    • irrational 4y ago
      That is factual data. Saying you won’t believe that a person is or is not thinking about something is entirely different.
      • Scea91 4y ago
        > I stopped reading at this point. Someone that is so certain that they say “I simply won’t believe you.” is too self-assured to be worth paying much attention to. Actually it is even more ironic. You are too self-assured that a multi-page article is not worth paying attention to because of a single sentence in it that irritates you.
• keshet 4y ago
  Somebody is still wrong on the internet
  • deleted 4y ago
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• galaxyLogic 4y ago
  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.
  • javajosh 4y ago
    >I think Dunning Krueger makes intuitive sense. 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.
    • robocat 4y ago
      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).
    • JetAlone 4y ago
      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.
• goosedragons 4y ago
  "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.
  • karpierz 4y ago
    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).
    • Tyr42 4y ago
      That's exactly the difference this article is driving at!
• lamontcg 4y ago
  I'm not a practicing statistician, so I'm uncertain how to weigh the two arguments here.
  • kurthr 4y ago
    I see what you did and I am completely uninformed in my certainty that Dunning-Kruger is wrong!
    • bombcar 4y ago
      I fall back to Sturgeon’s Law and assume 90% of everything, including me, is shit.
      • lamontcg 4y ago
        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.
        • 314 4y ago
          Based on your two comments in this thread I want to live in the world that coincides with your world view. It gives me hope.
      • rzzzt 4y ago
        This sounds like an extreme illustration of the Baader-Meinhof phenomenon.
• aaaronic 4y ago
  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).
  • ncmncm 4y ago
    "Most citations of D-K are examples of it."
  • robocat 4y ago
    Or perhaps your comment is the relatively rare meta-meta-DK effect.
  • jasonhansel 4y ago
    > 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 https://en.wikipedia.org/wiki/Nobel_disease
    • lamontcg 4y ago
      https://xkcd.com/793/ https://xkcd.com/793/ "there's nothing more annoying than a physicist encountering a new subject"
• rafaeltorres 4y ago
  Yeah, this makes sense to me. 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.
  • clwk 4y ago
    "Most believe themselves 'above average' at most things."
    • mewse 4y ago
      Most people have an above-average number of legs. There's really no contradiction there; all it takes is for there to be a couple low scores pulling the average down.
      • dragonwriter 4y ago
        > 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.
      • LaurieKoudstaal 4y ago
        In the context of the paper, we should be talking about the median, not the mean.
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  • topaz0 4y ago
    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.
• murrayb 4y ago
  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. Edit-see below I meant opposite not corollary.
  • smw 4y ago
    Isn't that the exact opposite of D-K?
    • murrayb 4y ago
      Doh corollary doesn't mean what I thought. I learned something thank you. I meant the opposite.
      • taeric 4y ago
        Perhaps you meant contrary?
        • murrayb 4y ago
          Nah I meant opposite. Just incompetent and unaware, there should be a name for it ;-)
• blamestross 4y ago
  Possibility 3 backed up by all the same data: 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.
  • _0ffh 4y ago
    I also recently though about this problem and came to the same hypothesis, which fits the Dunning-Kruger data perfectly.
  • majormajor 4y ago
    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?
    • blamestross 4y ago
      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.
  • twobitshifter 4y ago
    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.
• soVeryTired 4y ago
  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.
• haberman 4y ago
  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. It's true, the previous article (https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/ 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.
  • insaider 4y ago
    So true, the whole DK discourse is very rarely constructive. Except on HN of course ;)
  • jasonhansel 4y ago
    > 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.
• jldugger 4y ago
  > 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. [1]: https://en.wikipedia.org/wiki/Ecological_correlation https://en.wikipedia.org/wiki/Ecological_correlation [2]: https://replicationindex.com/2020/09/13/the-dunning-kruger-effect-explained/ https://replicationindex.com/2020/09/13/the-dunning-kruger-e...
  • _carbyau_ 4y ago
    Do they have to encourage 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.
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  • closed 4y ago
    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.
  • geysersam 4y ago
    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. Neither seems very plausible!
• parentheses 4y ago
  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.
  • sjmm1989 4y ago
    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.
  • jasonhansel 4y ago
    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."
    • AmericanChopper 4y ago
      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.
    • rootusrootus 4y ago
      > 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.
• photochemsyn 4y ago
  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." https://www.nytimes.com/1994/10/26/opinion/in-america-throwing-a-curve.html 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..."
  • anonymoushn 4y ago
    > 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.
• ipnon 4y ago
  It would be quite ironic if Dunning-Kruger opponents were arguing against its statistical validity with faulty statistical reasoning.
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• nosefrog 4y ago
  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. 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-kruger-effect-probably-not-real https://www.mcgill.ca/oss/article/critical-thinking/dunning-...
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  • civilized 4y ago
    > 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.
    • nosefrog 4y ago
      Sure, it's an interesting question, but if the way you're measuring it can't be distinguished from noise, you need to find a different measure.
• obastani 4y ago
  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. [1] https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/ https://economicsfromthetopdown.com/2022/04/08/the-dunning-k... [2] https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect [3] https://news.ycombinator.com/item?id=31036800 https://news.ycombinator.com/item?id=31036800
  • hgomersall 4y ago
    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.
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  • saurik 4y ago
• devit 4y ago
  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.
  • ta123457864 4y ago
    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.
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• marcholagao 4y ago
  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.
  • jameshart 4y ago
    This is such a bizarre argument. 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.
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    • slavik81 4y ago
      > 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.
      • notahacker 4y ago
        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.
• longtimegoogler 4y ago
  Well argued and I agree completely.
• MichaelBurge 4y ago
  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.
  • cortesoft 4y ago
    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.
    • etchalon 4y ago
      What's that line about half of all people being below average…
  • 0x20cowboy 4y ago
    60% of the time, it works every time.
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  • bmacho 4y ago
• quanto 4y ago
  The conclusion in the article: > 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.
  • boppo1 4y ago
    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.
    • quanto 4y ago
      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.
• zharknado 4y ago
  Thanks for writing! Really valuable rebuttal imo. 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.
• sumanthvepa 4y ago
  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... :-)
  • jasonhong 4y ago
    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.
  • weird-eye-issue 4y ago
    Sounds like DK in effect
    • djs070 4y ago
      Parent comment made no evaluation of their own ability, so I don’t see how it’s DK in effect at all.
      • weird-eye-issue 4y ago
        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
      • tunesmith 4y ago
        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.)
  • stagas 4y ago
• randcraw 4y ago
  As a novice on DK, it seems to me that, for DK to be 'suprising' (in the parlance of the OP), four phenomena must hold: 1) an incompetent person is poorer than average at self assessment of their skill 2) as a person's competence increases at a skill, their ability to self-assess improves, until they become 'expert' which is defined by underappreciating their own skill (or overappreciating the skill of others) 3) DK is surprising (interesting) only when some incompetent persons who suffer from DK cannot improve their performance, presumably because their poor self-assessment prevents their learning from experience or from others. 4) Worse yet, some persons suffering from DK cannot improve their performance in numerous skill areas, presumably because their poor self-assessment is caused by a broad cognitive deficit (e.g. political bias), preventing them from improving on multiple fronts (which are probably related in some thematic way). If DK is selective to include only one or two skill areas, as in case 3, that is not especially surprising, since most of us have skill deficits that we never surmount (e.g. bad at math, bad at drawing, etc). DK becomes surprising only in case 4, when we claim there is a select group of persons who have broad learning deficits, presumably rooted in poor assessment of self AND others — to wit, they cannot recognize the difference between good performance and bad, in themselves or others. Presumably they prefer delusion (possibly rooted in politics or gangsterism) to their acknowledgement of enumerable and measurable characteristics that separate superior from inferior performance, and that reflect hard work leading to the mastery of subtle technique. If case 4 is what makes DK surprising, then DK certainly is not described well by the label 'autocorrelation' — which seems only to describe the growth process of a caterpillar as it matures into a butterfly.
  • bryanrasmussen 4y ago
    >it seems to me that, for DK to be 'surprising' (in the parlance of the OP), four phenomena must hold: The surprising things about DK, to me at any rate, is how unvarying it is in application. Under DK people who are poor at something never think wow I really suck at this, or if they do they are such a minuscule part of the population that we can discount them. I've known lots of people who were not good at particular things and did not rate themselves as competent at it, although truth is they might have claimed competence if asked by someone they didn't want to be honest with.
• nokya 4y ago
  Thank you infinitely for taking the time to respond. I don't have this luxury in my life right now but I admit after reading the "original" post almost a fourth time, I was really hoping someone would take the time to explain why/how the author could be completely wrong (or not). Thanks.
• dahart 4y ago
  One of the best commentaries on DK is Tal Yarkoni’s, and he came to the (perhaps similar?) conclusion that DK is probably regression to the mean. https://www.talyarkoni.org/blog/2010/07/07/what-the-dunning-kruger-effect-is-and-isnt/ https://www.talyarkoni.org/blog/2010/07/07/what-the-dunning-... It bugs me that DK reached popular consciousness and get misinterpreted and misused more often than not. For one, the paper shows a positive correlation between confidence and skill. The paper is very clearly leading the reader, starting with the title. The biggest problem with the paper is not the methodology nor the statistics, it’s that the waxy prose comes to a conclusion that isn’t directly supported by their own data. People who are unskilled and unaware of it is not the only explanation for what they measured, nor is that even particularly likely, since they didn’t actually test anyone who’s verifiably or even suspected to be incompetent. They tested only Cornell undergrads volunteering for extra credit.
  • denton-scratch 4y ago
    If DK is regression to the mean (a view I find convincing) that doesn't mean the effect isn't real; i.e. one would still observe that people of low ability overestimate their ability, simply because there is more "room" for overestimates than underestimates. And v.v. Put differently, if everyone's estimate was exactly the mean, you'd still see a "DK effect".
    • dahart 4y ago
      I’m not sure I understand. If the effect shown in the paper is regression to the mean, then that does mean the paper doesn’t actually demonstrate what it claims to, right? I mean you can argue that the idea is still plausible, but this would mean that the paper doesn’t support the claim that low skill people overestimate themselves, right? It’s also an interpretation to focus on unskilled people as the explanation. DK’s data shows the very same effect on highly skilled people. The people in the top quartile were just as bad at self-estimating as the bottom quartile, yet the paper claims only the unskilled people were unaware! I recommend reading the DK paper. It didn’t test any people of low ability, and it did not evaluate skill in absolute terms. The sample size was tiny. The kids who participated were all earning extra credit in a class (it’s a self-selecting population that might have excluded both A students and F students.) The students were all Ivy League undergrads who might all overestimate their abilities precisely because they’re in a prestigious school and their parents told them they’re great. The paper didn’t test any actual low IQ population. The paper has methodology problems when it comes to non-native English speakers. It absolutely blows my mind that the paper is held up as evidence for some kind of universal human trait with such miniscule and completely questionable evidence. I have no doubt that some people overestimate their abilities in some situations. Like you, I’m sure, I’ve witnessed that. But as a commentary on all of humanity, I’m becoming convinced that the so-called DK effect does not exist, that they didn’t show what they claim to show. It doesn’t help that many replication attempts have not only failed to replicate, but have ended up showing the opposite effect: that for many kinds of skilled activities, people.
• 8note 4y ago
  The open question this raises to me is why a DK=true set of data would show up with the same graph as a uniformly random set What I'm really missing is a plot of the data without the aggregation. I find it very strange that X is broken down into quartiles but Y isn't, and when in quartiles, people estimated their skills relative to each other quite well: the line still goes up, and from bottom to top, would be a perfect X to X corelation
  • obastani 4y ago
    Uniformly random data means that someone’s perception of their ability is uncorrelated with their actual ability, which is exactly what DK=true is saying!
• darawk 4y ago
  I can't believe nobody has pointed out that the original article debunking the DK effect is in fact an example of the effect. Truly poetic.
  • spiderfarmer 4y ago
    It was pointed out multiple times in the original thread.
  • TrackerFF 4y ago
    Tbh, I only ever hear people reference DK when they're trying to point out that they're "working with morons" (in their opinion, of course).
    • Scea91 4y ago
      ...and claim they suffer from impostor syndrome themself.
• ComradePhil 4y ago
  If you measure competence as relative performance, a person cannot know how competent they are compared to others... because to do that correctly, they would not only have to know how much they know but also know how much other people know... preferably in relation to them. This is not possible, so the self-assessment data will be random because it is a random guess... so it does not correlate to actual performance or anything else for that matter. Hence, DK effect has to be a result of faulty statistical analysis. I believe we'd have completely different results if the question was framed differently: "how many do you believe you got right?". Then, more confident people, regardless of competence, would answer that they got more right and less confident people, again regardless of competence, would believe that they must have gotten more wrong than they did.
• etchalon 4y ago
  Something I generally keep in mind about articles posted to HN: A large portion of the HN audience really, really wants to think they're smarter than mostly everyone else, including most experts. Very few are. I'm certainly not. Articles which "debunk" some commonly held belief, especially those wrapped in what appears to be an understandable, logical, followable argument, are going to be cat nip here. Articles like this are even stronger cat nip. If a member of the HN audience wants to believe they're mostly smarter than mostly everyone else, that includes other members of the HN audience. So, whenever I read an article and come away thinking that, having read the article, I'm suddenly smarter than a huge number of experts, especially if, like the original article, it's because I understand "this one simple trick!", I immediately discard that knowledge and forget I read it. If the article is right, it will be debated and I'll see more articles about it, and it'll generate sufficient echoes in the right caves of the right experts. Once it does, I can change my view then. I am not a statistician, or a research scientist. I have no idea which author is right. But, my spider sense says that if dozens of scientific papers, written by dozens of people who are, failed to notice their "effect" was just some mathematical oddity, that'd be pretty incredible. And incredible things require incredible evidence. And a blog post rarely, if ever, meets that standard.
• kybernetikos 4y ago
  Would it be possible to understand the results differently? It looks to me that the data could be explained by the participants moderating their self assessment away from extremes or perhaps towards the population mean which is arguably not an unreasonable thing to do if your knowledge of the population mean is better than your knowledge of your own performance.
  • IshKebab 4y ago
    Yeah I agree that's the likely explanation. Nobody wants to admit that they're terrible and nobody wants to boast that they're the best and be proven wrong. So my suspicion is that the DK effect is not really a symptom of people's inability to accurately self-assess, but they're unwillingness to accurately report that self-assessment. And I don't think it is unique to self assessment either. It's common knowledge that ratings on a scale out of 10 for pretty much everything are nearly always between 6 and 9. I don't know how they did the experiment but I bet they'd get different results if the self-assessments were anonymous and accuracy came with a big financial reward. Anyway that's all irrelevant to the point of the article which I think is correct.
  • sandgiant 4y ago
    And this is why we need error bars on all plots. Looking at these plots there is no way to know whether people guessed uniformly or whether the self assessment is clustered around the mean.
• prvc 4y ago
  Just based on the graph just under the "The Dunning-Kruger Effect" section, one observation I'd like to present is that the subjects's numerical self-assessments fall into the same range as passing but non-stellar grades do in school. This may reflect a psychological bias in how the subjects use and understand percentages. Accordingly, that the two lines cross is a red herring.
• omnicognate 4y ago
  Gah, I wish I had time to fully read this and get into it, but I have to spend the next few hours driving. Unfortunately the original article isn't very clearly explained, and it's only on reading the discussion in the comments under it that it becomes clear what it's actually saying. The point is about signal & noise. Say your random variable X contains a signal component and a noise component, the former deterministic and the latter random. Say you correlate Y-X against X, and further say you use the same sample of X when computing Y-X as when measuring X. In this case your correlation will include the correlation of a single sample of the noise part of X with its own negation, yielding a spurious negative component that is unrelated to the signal but arises purely from the noise. The problem can be avoided by using a separate sample of X when computing Y-X. The example in the original "DK is autocorrelation" article is an extreme illustration of this. Here, there is no signal at all and X is pure noise. Since the same sample of X is used a strong negative correlation is observed. The key point though is that if you use a separate sample of X that correlation disappears completely. I don't think people are realising that in the example given the random result X will yield another totally random value if sampled again. It's not a random result per person, it's a random result per testing of a person. This is only one objection to the DK analysis, but it's a significant one AFAICS. It can be expected that any measurement of "skill" will involve a noise component. If you want to correlate two signals both mixed with the same noise sources you need to construct the experiment such that the noise is sampled separately in the two cases you're correlating. Of course the extent to which this matters depends on the extent to which the measurement is noisy. Less noise should mean less contribution of this spurious autocorrelation to the overall correlation. To give another ridiculous, extreme illustration: you could throw a die a thousand times and take each result and write it down twice. You could observe that (of course) the first copy of the value predicts the second copy perfectly. If instead you throw the die twice at each step of the experiment and write those separately sampled values down you will see no such relationship.
  • andersource 4y ago
    Hey omnicognate, good to see you here, appreciated our previous discussion. What you're saying is that we need to verify the statistical reliability of the skill tests DK gave, and to some extent that we need to scrutinize the assumption that there indeed is such a thing as "skill" to be measured in the first place. I hope we can both agree that skill exists. That leaves the test reliability (technical term from statistics, not in the broad sense). What's simulated by purely random numbers is tests with no reliability whatsoever. Of course if the tests DK gave to subjects don't actually measure anything at all, the DK study is meaningless. If that's what the original article's author is trying to say, they sure do it in a very roundabout way, not mentioning the test reliability at all. I'd be completely fine reading an article examining the reliability of the tests. Otherwise, I again fail to see how the random number analysis has anything to do with the conclusions of DK. In fact, DK do concern themselves with the test reliability, at least to some extent. That doesn't appear in the graph under scrutiny but appears in the study. If you assume the tests are reliable, and you also assume that DK are wrong in that people's self-assessment is highly correlated with their performance, and generate random data accordingly, you'll still get no effect even if you sample twice as you propose. > The key point though is that if you use a separate sample of X that correlation disappears completely Separate sample of X under the assumption of no dependence at all of the first sample, i.e., assuming there is no such a thing as skill, or assuming completely unreliable tests. So, not interesting assumptions, unless you want to call into question the test reliability, which neither you nor the author are directly doing.
• andi999 4y ago
  The relative DK effect not to exist would require clairevoyance from the participants. The non relative dk effect is more interesting.
• seniortaco 4y ago
  Beyond the validity of the statistical methods used.. can someone clarify what is the actual hypothesis we are debating about competence? And what does each article propose? My understanding is that the hypothesis is "Those who are incompetent overestimate themselves, and experts underestimate themselves". DK says: True DK is Autocorrelation says: ??? "I cant let go..." says: True? HN says: also True? Is there really any debate here? The "DK is Autocorrelation" article seems to be the only odd one out, and it's not clear if it even makes a proposal either way about the DK hypothesis. It talks about the Nuhfer study, but that seems Apples vs Oranges since it buckets by education level. Then it also points out that random noise would also yield the DK effect. But that also does not address the DK hypothesis, and it would indeed be very surprising if people's self evaluation was random! So should my takeaway here just be that the DK hypothesis is True and that this is all arguing over details?
  • vetleen 4y ago
    DK says: True DK is Autocorrelation says: The DK article is based on a false premise, we got to disregard it "I cant let go..." says: Actually, given that we assume people are somewhat capable of self-assessment, which is reasonable, "DK is Autocorrelation" is the one based on a false premise, and we should disregard that one instead, and not DK.
  • formerly_proven 4y ago
    > My understanding is that the hypothesis is "Those who are incompetent overestimate themselves, and experts underestimate themselves". The DK hypothesis is "double burden of the incompetent": "Because incompetent people are incompetent, they fail to comprehend their incompetence and therefore overestimate their abilities more than expertes underestimate theirs" Arguably the hypothesis that matches the data from the DK paper best is: "Everyone thinks they're average regardless of skill level"
    • dragonwriter 4y ago
      > The DK hypothesis is "double burden of the incompetent" The actual DK result (which is much criticized, but that's a different issue) was actually a pretty much linear relationship between actual relative performance and self-estimated relative performance, crossing over at about the 70th percentile. (Because there is more space below 70 than above, that also means that the very bottom performers overestimated their relative performance more than top performers underestimated, not because of any “double burden” (overstimation didn't rise faster as one moved below the crossover), but just because there was more space below the crossover point. > Arguably the hypothesis that matches the data from the DK paper best is: "Everyone thinks they're average regardless of skill level" If there was a perceptual nudge toward average relative performance, you'd expect a crossover at the median with a slope below 1, the nudge is toward a particular point above average.
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• t_mann 4y ago
  It can give us an indication of how the growth rate depends on size Except that what you've plotted there isn't the growth rate, but the absolute growth. Your argument for DK isn't convincing either, they claimed sth much stronger than that we can't assess our own skills.
• gverrilla 4y ago
  Science is in deep crisis. It's only utility today is supporting industry and some public infrastructure. Social sciences are a scam, being economics the greatest racket amongst them all.
• LudwigNagasena 4y ago
  The author seems to go completely astray at some point. > “Never assume dependence” gets so ingrained that people stubbornly hold on to the argument in the face of all the common sense I can conjure. If you still disagree that assuming dependence makes more sense in this case, I guess our worldviews are so different we can’t really have a meaningful discussion. Hypothesis testing is concerned with minimization of Type I and Type II errors. In the Neyman-Pearson framework this calls for specific choice of the null hypothesis. Of course nothing prevents you to define the sets for H0 and H1 as arbitrarily as you want as long as you can mathematically justify your results. It seems like the author fundamentally misunderstands the basics of statistics.
• NumberCruncher 4y ago
  > We don’t need statistics to learn about the world. A sentence, written by the author on, commented by me on and read by the HN community on devices, which exist only thanks to 80-90 years of rigorous, statistics based QA in engineering, especially in mechanical/hardware engineering. Anyhow, after spending years on a team filled with social science PHDs, I would not waste my time on reading papers about statistical analysis done by social scientist.
  • Traster 4y ago
    I don't think your interpreting the sentence correctly, I think it's more correct to read it as: > We don't [only] need [the formal discipline of mathematics known as] statistics to learn about the world. Sure, there are things you can only functionally ascertain through statistical analysis. But not everything in the world needs rigorous statistics.
    • NumberCruncher 4y ago
      > I don't think your interpreting the sentence correctly And I think you are injecting the words "only", "there are" and "everything" here and there just to change the meaning of the sentences I quoted and I have written...
    • snovv_crash 4y ago
      We don't need != We don't only need. First one means "throw it away". Second one means "add other things too".
• danbruc 4y ago
  Read the actual paper [1], there is so much more than those charts. They ask for an assessment of the own test score and an assessment of the ranking among the other participants to distinguish between misjudgments of the own abilities and the abilities of others. They give participants access to the tests of other participants and check how this affects self assessments - competent participants realize that they have overestimated the performance of other participants and now assess their own performance as better than before, incompetent participants do not learn from this and also assess their performance even better than before. They randomly split participants into two groups after a test, give one group additional training on the test task, and then ask all of them to reconsider their self assessments - incompetent participants that received additional training are now more competent and their self assessment becomes more accurate. This is not everything from the paper and probably also somewhat oversimplified, I just want to provide a better idea of what is actually in there. Everyone is free to question the results, but after actually reading the entire paper I can confidently say that poking a bit at the correlation in the charts falls way short of undermining the actual findings from the paper. The actual results are much more detailed and nuanced than two straight lines at an angle. [1] https://www.researchgate.net/publication/12688660_Unskilled_and_Unaware_of_It_How_Difficulties_in_Recognizing_One's_Own_Incompetence_Lead_to_Inflated_Self-Assessments https://www.researchgate.net/publication/12688660_Unskilled_...
  • mike_hearn 4y ago
    I think if you wanted to poke holes in the paper you'd start with the generic issues that are typical to much psychological research: 1. It uses a tiny sample size. 2. It assumes American psych undergrads are representative of the entire human race. 3. It uses stupid and incredibly subjective tests, then combines that with cherry picking: "Thus, in Study 1 we presented participants with a series of jokes and asked them to rate the humor of each one. We then compared their ratings with those provided by a panel of experts, namely, professional comedians who make their living by recognizing what is funny and reporting it to their audiences. By comparing each participant's ratings with those of our expert panel, we could roughly assess participants' ability to spot humor ... we wanted to discover whether those who did poorly on our measure would recognize the low quality of their performance. Would they recognize it or would they be unaware?" In other words, if you like the same humor as professors and their hand-picked "joke experts" then you will be assessed as "competent". If you don't, then you will be assessed as "incompetent". Of course, we can already guess what happened next - their hand picked experts didn't agree on which of their hand picked jokes were funny. No problem. Rather than realize this is evidence their study design is maybe not reliable they just tossed the outliers: "Although the ratings provided by the eight comedians were moderately reliable (a = .72), an analysis of interrater correlations found that one (and only one) comedian's ratings failed to correlate positively with the others (mean r = -.09). We thus excluded this comedian's ratings in our calculation of the humor value of each joke" The fact that this actually made it into their study at all, that peer reviewers didn't immediately reject it, and that the Dunning-Krueger effect became famous, is a great example of why people don't or shouldn't take the social sciences seriously.
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• torginus 4y ago
  When I saw the graphs in the original article I immediately came to a different conclusion - that people with a given amount of skill have low confidence in their ability to gauge how skilled they are compared to an arbitrary group. For example, if someone gave me (or you) a leetcode-style test, and told me I'd be competing against a sample picked from the general population, and ask me how well I did, I'd probably rate myself near the top with high confidence. Conversely, if my competitors were skilled competitive coders, I'd put myself near the bottom, again with high confidence. Now, if I had to compete with a different group, say my college classmates, or fellow engineers from a different department, I'd be in trouble, if I scored high, what does that mean? Maybe others scored even higher. Or if I couldn't solve half of the problems, maybe others could solve even less - point is I don't know. In that case the reasonable approach for me would be to assume I'm in the 50th percentile, then adjust it a bit based on my feelings - which is basically what happened in this scenario, and would produce the exact same graph if everyone behaved like that. No need to tell tall tales of humble prodigies and boastful incompetents.
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• wodenokoto 4y ago
  This is a follow up/reaction to an article that hit the front page a few days ago. Might be worth to check out the discussion there as well: https://news.ycombinator.com/item?id=31036800 https://news.ycombinator.com/item?id=31036800
• john_pryan 4y ago
  For anyone who is interested in playing around with these charts, the various assumptions that under pin them etc. I've thrown together a colab notebook as a starting point. Observation: if you rank via true "skill" and assume for a particular instance the predicted performance and observed performance are independent but both have the true skill as their mean you dont observe the effect. CC of 0.00332755. If you rank via observed performance and plot observed vs predicted the effect is there. CC of -0.38085757. This is assuming very simple gaussian noise which is not going to be accurate especially as most of these tasks have normalised scores. Edit: fixed wrong way around https://colab.research.google.com/drive/1Vy7JjkywxwEP8nfR6oSV0az0cVUKTyLR?usp=sharing https://colab.research.google.com/drive/1Vy7JjkywxwEP8nfR6oS...
  • andersource 4y ago
    Thanks John! Very interesting. What your simulation includes and the original article didn't (and I didn't touch at all in my article) is the statistical reliability of the tests they administered. Where you got a CC of -0.38 you used equal reliability (/ unreliability) of the skill tests and self-assessments. You can see that as you increase the test reliability, the CC shrinks and the effect disappears. I have no idea what's the actual reliability of the DK tests, they do seem to consider that but maybe not thoroughly enough. In my view it's very fair to criticize DK from that angle. But that would require looking at the actual tests and their data. My point being, that any purely random analysis is based on assumptions that can easily be tweaked to show the same effect, the opposite effect, or no effect at all.
    • john_pryan 4y ago
      That's a nice spot about the decreasing CC as we increase accuracy! My hypothesis would be that some of the DK effect in the original paper may be down to an effect like this (as suggested in the original article) but that asserting it is completely incorrect because of it is premature. We'd need access to more data to verify that the level of reliability was sufficiently acceptable.
      • 4y ago
• emsign 4y ago
  Sounds like the premise is flawed. He's assuming kids are good at getting another 10 minutes before bedtime. All of them? What about those who fail? Those that don't even try? The issue is not the way our brains generalize, but that you are using just one brain, one life's experience.
• tpoacher 4y ago
  Great article. Very nicely written. In partial "defense" of the "autocorrelation" article, the author was in fact arguing against their own perceived definition of DK, not what most people consider to be DK. They just didn't realise it. Which is an all too common thing to begin with. (that particular article pulled the same stunt with the definition of the word 'autocorrelation', after all).
• jcranberry 4y ago
  I don't really understand the article. My understanding was that the mistake was that the error bounds differ depending on the test score from the original DK paper. A test score of 0 or 100 means a potential error of 0-100, whereas a test score of 50 means a potential error of 50. So if you take a group of people who score 0-25 points, if their self-assessment is completely random you'd still see a bias of overestimating score,because people who would give themselves a lower score if possible are unable to.
  • oh_my_goodness 4y ago
    The charts make it clear that people's self-assessment was (roughly) independent of their skill level. It's not obvious that students' self-assessment would be mostly random / unrelated to skill level. For me that's a non-obvious result. If people wander off through the verbiage of any article, where the chatter isn't supported by data, sure, they'll tend to get speculation.
    • jcranberry 4y ago
      I don't really understand what you're saying. Are you the saying the charts don't actually make it clear, or that they make it clear that self assessment is independent but not necessarily uniform?
      • oh_my_goodness 4y ago
        The charts make it clear that self-assessment was roughly unrelated to ability. That's not an artifact of autocorrelation, instead it appears to be an experimental result.
• brodouevencode 4y ago
  Question for you folks that are smarter than me (see what I did there?) - DK has surfaced a lot here and in the online world more broadly with seemingly increased frequency. Why do you think that is?
• bannedbybros 4y ago
• TimPC 4y ago
  I feel like the author read the autocorrelation result, hated it and ignored the central point. There are ways to bucket data that removes the autocorrelation and in those experiments we also see the DK effect disappear. Trying to argue that we should study the effect with the autocorrelation present but ignore the autocorrelation for 'reasons' is not the way forward.
• semanticjudo 4y ago
  We’ll done. I read the autocorrelation post when it came out a couple weeks back and it didn’t sit right with me. But I didn’t have the motivation to figure out why. Your explanation resonates perfectly with my initial (snap) intuition and I thank you for taking the time to write it out and post!