Re: Re:
I'm shaking my head at the sheer amount of BS here. You guys are both spouting nonsense.
First of all, just because a model has a lot of accuracy doesn't mean it is a) replicable outside the sample, or b) it is a good model. A model may be used for a) prediction or b) explanation. A model that does b) doesn't necessarily do a). A key example of that is a model that contains a high degree of multicollinearity.
That "two views on statistics" comment? Complete and utter crap. Look, you are probably 100 times better than me at machine learning, but the stuff you are saying about statistics is nonsense. 100% crap. There is no "Chomsky" or "Google" view. Good grief. Go read about Fisher, Gosset, Pearson, or Neyman.
Second, Swanson, you shouldn't be agreeing, because he says exactly the opposite of what you are saying. He's not talking about "deterministic" models. Those by definition have 100% accuracy, because they have no randomness. That last sentence you wrote is sheer nonsense just for the sake of sounding intelligent.
Don't pull that stuff. You're good at what you do, so do it. And stick to it.
ScienceIsCool said:vedrafjord said:King Boonen said:I think people are missing the really interesting thing here. It's not whether this data shows doping or not. It's that this data matches almost exactly the w/kg calculations of Vayer and Ferrari(?), even though Brailsford dismissed them as peseudo-scientific rubbish.
Exactly, there are two views on statistics. My background is in machine learning so I'll call them the Chomsky view and the Google view, as described here http://norvig.com/chomsky.html. The Chomsky/Brailsford view is that you have to perfectly model every conceivable factor with real physics etc and the model has to mirror reality. The Google/ammattipyoraily view is that the predictive accuracy of the model is what matters and not the internals.
My view is that the Chomsky way leads to whataboutery and nothing ever being done. If you have a model and it has very high accuracy despite leaving out certain variables, I think it's obvious that those variables aren't important.
I'm very much of that view as well. Often, what you're after in the lab is a parametric response. As I turn this knob, what happens to the output - that kind of thing. Use that data to create a model. Working from first principles to write a deterministic equation from which to model your data is a great big time suck and that's about it.
John Swanson
I'm shaking my head at the sheer amount of BS here. You guys are both spouting nonsense.
First of all, just because a model has a lot of accuracy doesn't mean it is a) replicable outside the sample, or b) it is a good model. A model may be used for a) prediction or b) explanation. A model that does b) doesn't necessarily do a). A key example of that is a model that contains a high degree of multicollinearity.
That "two views on statistics" comment? Complete and utter crap. Look, you are probably 100 times better than me at machine learning, but the stuff you are saying about statistics is nonsense. 100% crap. There is no "Chomsky" or "Google" view. Good grief. Go read about Fisher, Gosset, Pearson, or Neyman.
Second, Swanson, you shouldn't be agreeing, because he says exactly the opposite of what you are saying. He's not talking about "deterministic" models. Those by definition have 100% accuracy, because they have no randomness. That last sentence you wrote is sheer nonsense just for the sake of sounding intelligent.
Don't pull that stuff. You're good at what you do, so do it. And stick to it.
