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> The D-K hypothesis is that self assessment and actual performance are less correlated for weaker than higher performing individuals. Isn't that what the grap

> The D-K hypothesis is that self assessment and actual performance are less correlated for weaker than higher performing individuals. Isn't that what the graph shows? The bottom quartile group is guessing almost 50 percentile points higher than their actual performance, whereas the top quartile is at most 15 points off. They're all guessing somewhere between the 60th and 75th percentiles (i.e. "I'm a bit better than average") - with some upwards trend since the high performers seem to at least know they have some skill, although not very accurately. It's just that for the poor performers, a guess of the 60th percentile wayyy off the mark.

somenameforme 3y ago
EDIT: Something important for the rest of this post. In case it's not clear, the graph is showing your percentile ranking within the group - not your actual score. Nope, because there's an interesting statistical trick in play. Imagine you take 100 highly skilled physicists and give them some lengthy series of otherwise relatively basic physics questions. Everybody is going to rate their predicted performance as high. But some people will miss some questions simply due to silly mistakes or whatever. And those people would end up on the bottom 10% of this group, even if the difference between #1 and #100 was e.g. 0.5 points. Graph it as D-K did, and you'd show a huge Dunning Kruger effect, even when there is obviously nothing of the sort. In fact the fewer differences in ability within a group, and the greater the relative ease of a task, the bigger the Dunning-Kruger effect you'd show. Because everybody will rate themselves relatively high, but you will always have a bottom 10%, even if they are practically identical to the top 10%. You can see this most clearly in the original paper. They carried out 4 experiments. The one that was most objective and least subject to confounding variables was #2, where they asked people a series of LSAT based logic questions, and assessed their predicted vs actual results. And there was very little difference. Quoting the paper, "Participants did not, however, overestimate how many questions they answered correctly, M = 13.3 (perceived) vs. 12.9 (actual), t < 1. As in Study 1, perceptions of ability were positively related to actual ability, although in this case, not to a significant degree." Yet look at the graph for it, and again it shows some seemingly large D-K effect. And there's even more issues with D-K, and especially experiment #1 (which is the one with the prettiest graph by far), but that's outside the scope of this post. I'm happy to get into it, if you are though. I find this all just kind of shocking and exceptionally interesting! I've referenced the D-K effect countless times in the past, never again after today! [1] - https://sci-hub.se/10.1037/0022-3514.77.6.1121 https://sci-hub.se/10.1037/0022-3514.77.6.1121
- dahart 3y ago
  Yes yes yes! I’m in the very same boat, and came to an epiphany that the ranking trick here, combined with some subjective questions (ability to appreciate humor - seriously!?), that these things hide almost everything about actual skill. Not only does it amplify mistakes, it also forces the participants to have to know something about their cohort. Having to guess your ranking fully explains the less than perfect correlation. It also undermines all claims about competence and incompetence. They’re not testing skill, they’re only testing ability to randomly guess the skill of others. What about the slight bias upwards? Well, what exactly was the question they asked? It’s not given in the paper. They were polling only Cornell undergrads looking for extra credit. What if the question somehow accidentally or subtly implied they were asking about the ranking against the general population, and then they turned around and tested the answers against a small Cornell cohort? I just went and looked at the paper again and noticed that the descriptions of the ranking question changed between the various “studies” with the first one comparing to the “average Cornell student” (not their experiment cohort!). The others suggest they’re asking a question about ranking relative to the class in which they’re receiving extra credit. Curiously study 4 refers to the ranking method of study 2 specifically, and not 3. The class used in study 4 was a different subject than 2 & 3. How they asked this question could have an enormous influence on the result, and they didn’t say what they actually asked. Cornell undergrads are a group of kids that got accepted to an elite school and were raised to believe they’re better than average. Whether or not all people believe they’re better than average, this group was primed for it, and also have at least one piece of actual evidence that they really are better than average. If these were majority freshmen undergrads, they might be especially in calibrated to the skills of their classmates. In short, the sample population is definitely biased, and the potential for the study to amplify that bias is enormous. The paper uses suggestions and jumps to hyperbolic conclusions throughout. I’m really surprised that evidence and methodology this weak claims to show something about all of humanity and got so much attention.

somenameforme 3y ago

EDIT: Something important for the rest of this post. In case it's not clear, the graph is showing your percentile ranking within the group - not your actual score. Nope, because there's an interesting statistical trick in play. Imagine you take 100 highly skilled physicists and give them some lengthy series of otherwise relatively basic physics questions. Everybody is going to rate their predicted performance as high. But some people will miss some questions simply due to silly mistakes or whatever. And those people would end up on the bottom 10% of this group, even if the difference between #1 and #100 was e.g. 0.5 points. Graph it as D-K did, and you'd show a huge Dunning Kruger effect, even when there is obviously nothing of the sort. In fact the fewer differences in ability within a group, and the greater the relative ease of a task, the bigger the Dunning-Kruger effect you'd show. Because everybody will rate themselves relatively high, but you will always have a bottom 10%, even if they are practically identical to the top 10%. You can see this most clearly in the original paper. They carried out 4 experiments. The one that was most objective and least subject to confounding variables was #2, where they asked people a series of LSAT based logic questions, and assessed their predicted vs actual results. And there was very little difference. Quoting the paper, "Participants did not, however, overestimate how many questions they answered correctly, M = 13.3 (perceived) vs. 12.9 (actual), t < 1. As in Study 1, perceptions of ability were positively related to actual ability, although in this case, not to a significant degree." Yet look at the graph for it, and again it shows some seemingly large D-K effect. And there's even more issues with D-K, and especially experiment #1 (which is the one with the prettiest graph by far), but that's outside the scope of this post. I'm happy to get into it, if you are though. I find this all just kind of shocking and exceptionally interesting! I've referenced the D-K effect countless times in the past, never again after today! [1] - https://sci-hub.se/10.1037/0022-3514.77.6.1121 https://sci-hub.se/10.1037/0022-3514.77.6.1121

dahart 3y ago
Yes yes yes! I’m in the very same boat, and came to an epiphany that the ranking trick here, combined with some subjective questions (ability to appreciate humor - seriously!?), that these things hide almost everything about actual skill. Not only does it amplify mistakes, it also forces the participants to have to know something about their cohort. Having to guess your ranking fully explains the less than perfect correlation. It also undermines all claims about competence and incompetence. They’re not testing skill, they’re only testing ability to randomly guess the skill of others. What about the slight bias upwards? Well, what exactly was the question they asked? It’s not given in the paper. They were polling only Cornell undergrads looking for extra credit. What if the question somehow accidentally or subtly implied they were asking about the ranking against the general population, and then they turned around and tested the answers against a small Cornell cohort? I just went and looked at the paper again and noticed that the descriptions of the ranking question changed between the various “studies” with the first one comparing to the “average Cornell student” (not their experiment cohort!). The others suggest they’re asking a question about ranking relative to the class in which they’re receiving extra credit. Curiously study 4 refers to the ranking method of study 2 specifically, and not 3. The class used in study 4 was a different subject than 2 & 3. How they asked this question could have an enormous influence on the result, and they didn’t say what they actually asked. Cornell undergrads are a group of kids that got accepted to an elite school and were raised to believe they’re better than average. Whether or not all people believe they’re better than average, this group was primed for it, and also have at least one piece of actual evidence that they really are better than average. If these were majority freshmen undergrads, they might be especially in calibrated to the skills of their classmates. In short, the sample population is definitely biased, and the potential for the study to amplify that bias is enormous. The paper uses suggestions and jumps to hyperbolic conclusions throughout. I’m really surprised that evidence and methodology this weak claims to show something about all of humanity and got so much attention.