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Okay well in that case the article is deficient (I skimmed it, assuming it was making the same case I already knew about[1] -- my bad). You can actually reprodu
by _dain_ 5y ago
Okay well in that case the article is deficient (I skimmed it, assuming it was making the same case I already knew about[1] -- my bad). You can actually reproduce the DK graph without supposing that estimation bias depends on skill. There is an interactive visualization here[2] that does precisely that (click on the "line plot w/ centiles" tab). You can adjust the parameters to see how it behaves.
[1] https://news.ycombinator.com/item?id=31038386 https://news.ycombinator.com/item?id=31038386
[2] http://emilkirkegaard.dk/understanding_statistics/?app=Dunning_Kruger http://emilkirkegaard.dk/understanding_statistics/?app=Dunni...
- nbernard 5y agoThanks for the link! I see what you mean, but is using the "general overestimation" slider actually unbiased? I don't really understand what it is supposed to represent. Looking at the code, it seems to be only an additive term that shifts the plot upwards, but that does not explain anything (actually, in the code, the variable is called "up_bias", and could correspond to an actual DK effect...).
- _dain_ 5y agoPer DK's hypothesis, the added bias would be a decreasing function of underlying skill, not a constant as it is here. I think this is the only way to define it sensibly. It's easier to see what it's doing if you look at the scatterplot, although it's kind of annoying because the axes are flipped and it keeps the view centred on the data, so the points stay still and the axes move around them. Basically it's just translating all the points uniformly along the estimated score axis.