The problem here is in my opinion that statistics is taught in way to "isolated" fashion. I hated math related subjects all the way through highschool and undergraduate levels, but as soon as it went towards applying statistics even on simple survey studies it became interesting. Not the "math" but the "discovery" should be in the center. If you talk to social science people they often have a deep aversion against "numbers", so making it as accessible as possible early in the career might be essential.
In my experience, economics is the exception. Many of the economists I've worked with know their statistics, and they put in a lot of work to make sure their models agree with econometric or time series theory (that's possibly selection bias, though, given where I've worked).
Also, apologies for perpetuating the "arrogant economist" meme. Just reporting what I've experienced. :P
When you study psychology you don’t expect to sit around and do math all the time.
When you study biology you don’t expect to sit around and do math all the time.
When you study economics (especially if you actually want to become and economist) you do expect to sit around and do math all the time.
For many biologists and social scientists statistics classes must seem like a chore that has little to do with what they actually want to do. For economists statistics is just another one of their math tools.
Agreed--that's how I was encouraged to think about statistics in my grad classes + research programs. They're tools for getting things done correctly, so they need to be understood thoroughly--no different than a given programming language.
I read a couple of papers on the backlash (even by expert economists) against the heavy emphasis on mathematics in economics, which, IIRC from the papers, began back in the 1940s. Basically, economists work hard producing mathematically coherent models that aren't applicable to the real world.
This is a good point, and it's a problem in theoretical economics. Some economic models abstract away some important real-world conditions.
My experience, though, is with applied macroeconomics. We'd derive models consistent with theory, then make sure we avoided a number of time series pitfalls (autocorrelation, mistaking cointegration for correlation, overspecifying a model) using a variety of statistical techniques.
I'm not a hardcore statistics person, since I only took enough to get me through ECE, but I think this is why I always had a problem with softer sciences (undergraduates, maybe graduate students are better). Things always boiled down to, "look, it's obvious." My background simply makes me think the obvious isn't always right.
Funnily enough that’s actually one of the reasons why I have always been fascinated by social science. Not as something where the results and methods are fascinating but rather as something where much is to be done and all problems are hard to solve.
During the last century empiricism’s role in the social science has been growing but social science as an empirical science is still relatively young and, I think more importantly, it’s not really dominated by empiricism the same way, say, physics is.
I think, in general, I find some of academic's dogma a bit off-putting. I always approach things in the most classical sense--that is, everything is relatively true. Not to dismiss empirical data though. For example, people seem entrenched in saying dark matter is X or will do Y, but even people who helped discover its possibility say to be cautious of making such statements. When people come out and say it's not definitive in its existence (yet), they tend to get lambasted. I know there is always politics and evangelizing in every human situation, but my childhood notions of a pure method/approach seem a bit jaded now. Perhaps, some of the softer sciences need to study inter-academic hard science ;)
