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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 zer

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 5y ago
I also recently though about this problem and came to the same hypothesis, which fits the Dunning-Kruger data perfectly.
majormajor 5y 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 5y 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 5y 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.
deleted 5y ago
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_0ffh 5y ago

I also recently though about this problem and came to the same hypothesis, which fits the Dunning-Kruger data perfectly.

majormajor 5y 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 5y 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 5y 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.

deleted 5y ago

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