That’s stupid, though. If you can explain 11% of the variance of some noisy phenomenon like cognitive and behavioral flexibility, that’s noteworthy. They tested both linear and quadratic terms, and the quadratic one worked better in terms of prediction, and is also an expression of a meaningful theoretical model, rather than just throwing higher polynomials at it for the fun of it. Quadratic here also would coincide with some homogenizing mechanism at the two ends of the age distribution.
Maybe, yeah, but I kinda get annoyed at this kinda dismissiveness - it’s a type of vague anti-science or something like that. Like… Sure, overfitting is a potential issue, but the answer to that isn’t to never fit any curve when data is noisy, it is (among other things) to build solid theories and good tests thereof. A lot of interesting stuff, especially behavioral things, is noisy and you can’t expect to always have relationships that are simple enough to see.
You’re probably right. But also, I was annoyed, not trying to convince. Maybe not the best place to post from. :)
But I have eyes and the curve they picked as best fit is really poorly fitting. It’s such a poor fit that is almost in a dead zone of the random points.
DonPiano@feddit.org 10 hours ago
That’s stupid, though. If you can explain 11% of the variance of some noisy phenomenon like cognitive and behavioral flexibility, that’s noteworthy. They tested both linear and quadratic terms, and the quadratic one worked better in terms of prediction, and is also an expression of a meaningful theoretical model, rather than just throwing higher polynomials at it for the fun of it. Quadratic here also would coincide with some homogenizing mechanism at the two ends of the age distribution.
toynbee@lemmy.world 8 hours ago
Whether you’re right or wrong, starting your argument with “that’s stupid, though” is unlikely to convince many.
DonPiano@feddit.org 58 minutes ago
Maybe, yeah, but I kinda get annoyed at this kinda dismissiveness - it’s a type of vague anti-science or something like that. Like… Sure, overfitting is a potential issue, but the answer to that isn’t to never fit any curve when data is noisy, it is (among other things) to build solid theories and good tests thereof. A lot of interesting stuff, especially behavioral things, is noisy and you can’t expect to always have relationships that are simple enough to see.
You’re probably right. But also, I was annoyed, not trying to convince. Maybe not the best place to post from. :)
TimewornTraveler@lemmy.dbzer0.com 7 hours ago
well it convinced me, but I’m stupid and already made up my mind that I wanted to see a reply like that
onslaught545@lemmy.zip 9 hours ago
But I have eyes and the curve they picked as best fit is really poorly fitting. It’s such a poor fit that is almost in a dead zone of the random points.
DonPiano@feddit.org 1 hour ago
I dunno, the point cloud looks to me like some kinda symmetric upward curve. I’d’ve guessed maybe more like R^2=.2 or something in that range, though.
But also: This is noisy, it’s cool to see anything.