The author's example with random points is bad because you might reasonably expect people to behave differently than uniform random points.
It'd be reasonable to expect that people who are good at a thing estimate that they are good at it, and that people who are bad at a thing, estimate that they're bad at it. I mean, my kids love math and always estimate themselves to do well on math tests (and they usually do). They have classmates loudly detest math, estimate they'll do badly, and often do (at least somewhat). Similarly I'm a bad cook and I have no doubt that if I join a cooking contest, I'll get few jury points. The expected data is correlated.
So if a study finds that, well actually, the data is not at all that correlated! Lots of people who estimate that they'll do fine actually don't, and equally many people who estimate that they'll do badly, actually do fine (ie it looks like uniform random data), then that's surprising, and that's the D-K effect.
Right? I'm no statistician at all so I might be missing something.
The author's example with random points is bad because you might reasonably expect people to behave differently than uniform random points.
It'd be reasonable to expect that people who are good at a thing estimate that they are good at it, and that people who are bad at a thing, estimate that they're bad at it. I mean, my kids love math and always estimate themselves to do well on math tests (and they usually do). They have classmates loudly detest math, estimate they'll do badly, and often do (at least somewhat). Similarly I'm a bad cook and I have no doubt that if I join a cooking contest, I'll get few jury points. The expected data is correlated.
So if a study finds that, well actually, the data is not at all that correlated! Lots of people who estimate that they'll do fine actually don't, and equally many people who estimate that they'll do badly, actually do fine (ie it looks like uniform random data), then that's surprising, and that's the D-K effect.
Right? I'm no statistician at all so I might be missing something.