[foobar1967]

Wow, I've seen so many comments by climate change deniers using arguments so similar to yours that I literally had deja vu.

The horseshoe nature of politics will never cease to amuse me, though; thanks for the example.

Also, if a hypothesis is supported by simple studies but falls apart under more complex ones, it might be too simplistic a hypothesis. Almost as if sexism (and sexism guilt-slinging) wasn't an entirely black-and-white problem. Who'd have thought?

[dang]

Please don't add snark into inflammatory comments about already divisive topics. That's destructive of the kind of discussion we're hoping for here.

https://news.ycombinator.com/newsguidelines.html

https://news.ycombinator.com/newswelcome.html

[saosebastiao]

Climate Change Deniers are some of the biggest users of the flawed analyses that I'm talking about. For example, several climate change deniers argue that if you control for urban heat island effects, the magnitude of the human component decreases substantially. Such willy-nilly expansions of model complexity fall apart when modeled more rigorously [0][1].

You'll notice that I've never claimed that new parameters universally make the model worse. All of the common Information Criterions [2][3][4] are numerically capable of improvement of model quality with increases in parameter space...it's just unlikely. Such p-value hunting might give you the p-value you're looking for, but it is very unlikely to improve the model.

Sexism is a hard problem. Throwing variables wantonly at models until sexism disappears isn't doing anything for the problem...it's nothing more than a pseudoscientific way of pretending it doesn't exist.

[0] https://www.skepticalscience.com/urban-heat-island-effect.ht...

[1] http://news.stanford.edu/news/2011/october/urban-heat-island...

[2] https://en.wikipedia.org/wiki/Bayesian_information_criterion

[3] https://en.wikipedia.org/wiki/Deviance_information_criterion

[4] https://en.wikipedia.org/wiki/Akaike_information_criterion