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The key claim: "Random data actually mimics the effect really well." This makes some sense. If people are asked to guess a number between 1 and 6 and then roll
by brlewis 2mo ago
The key claim: "Random data actually mimics the effect really well."
This makes some sense. If people are asked to guess a number between 1 and 6 and then roll a die, the people who roll low are more likely to overestimate and the people who roll high are more likely to underestimate. But the key is precisely how well random data mimics the effect.
- oulipo 2mo agoIndeed. And the obvious reason is that when you simulate the "self-assessment" using a Gaussian noise around the "actual intelligence", and clamp it to [0, 100] so that it doesn't go "out of bound" (eg "negative intelligence" is not allowed), you will necessarily skew the low scores upward and the high scores downwards. But it's not because "some statistical model exhibit a bias that's similar to the result" that this implies "therefore the result is a statistical error"... that's a backward reasonning
- 5555watch 2mo agoThey're simulating randomness incorrectly: relationship between true and perceived will average 0.5, not 0; and bias will average 50%, not 0%. That's why their "random data" is sloped. Add negative relationship and negative bias, and the random data will act as intended - hovering randomly around 50%.
- algoth1 2mo agoThey, themselves, show the Dunning-Kruger effect by overestimating how much they understand randomness
- speerer 2mo agoIs this correct? I was fairly certain that the correlation between random data points approached 0. Is it also plausible that the relationship between true and percieved is wholly independent (as this presumes)?
- 5555watch 2mo agoI mean in their code the simulation is incorrect. Someone found the source: https://github.com/pem725/Dunning-Kruger https://github.com/pem725/Dunning-Kruger They're sampling slope and bias for the line from U[0,1] and U[0,100], the expected values of which will be 0.5 and 50. That's why they get what they get. They say that randomness is any positively sloped line with any positive bias. So they assume dependence, just a very high variance of it. It's incorrect as they miss the negative half of the parameters - it would then correctly yield zeroes, for the presumed independence.
- deleted 2mo ago[deleted]
- Jensson 2mo agoNo, the argument is that the best person cannot overestimate his own rank, and the worst person cannot underestimate it. The better you are the less room there is for you to overestimate your skill, second place can at most be off by one etc. This effect would disappear almost completely if they instead of estimating their rank they estimated their score, since then unless the test is so easy the best scores perfectly there will be a lot of room for everyone to overestimate and underestimate themselves. But as is when the top 10% all estimate themselves to be in the top 10%, you will say they are underestimate themselves since on average the top 10% are in the top 5%. At the same time if the bottom 10% say they are in the bottom 10%, you will say they overestimate themselves since actually on average they are bottom 5%. But both these groups were making the same mistake, and its impossible for that not to happen unless everyone is perfect.
- brlewis 2mo ago>This effect would disappear almost completely if they instead of estimating their rank they estimated their score In the article, in the section "The effect is in the noise", just before the graphs, it says they estimated their score. Where did you get that they estimated their rank?