Back on track

I was going to make this post a Wednesday Working Paper, but because of my fantastic Seattle vacation (and less fantastic return to 2 vet trips in as many days with my cat), I haven't actually read anything new. Sorry I'm not sorry. To get the ball rolling agin, I want to highlight two great websites that were brought to my attention this week (both via Twitter. Have I mentioned my ongoing love affair with Twitter yet?)

Great data visualization or  the greatest  data visualization? Proof that both analysis of and presentation of (social science) data is hard.

Great data visualization or the greatest data visualization? Proof that both analysis of and presentation of (social science) data is hard.

First, FiveThirtyEight has a really nice piece on the state of science. Like the Economist article I blogged about a little while ago (first link to my own blog - oooh, meta), this post has an interactive infographic where you can play with p-hacking, this time using actual data to show statistically significant effects of Republicans/Democrats on the economy. The article does a nice job explaining potentially complex issues, like p-hacking, differences in methodological approaches by different disciplines, and the degree to which science is self-correcting, in a digestible way.  As a (social) scientist myself, I appreciate the article's headline and subtitle: ``Science Isn't Broken - It's just a hell of a lot harder than we give it credit for.'' Truth. One important thing missing from this article, though, is that the author spends essentially zero time talking about causality. The p-hacking exercise (and, as far as I can tell, the fascinating soccer player example...which includes an author from BITSS, Garret Christensen) deals only with correlations. Figuring out whether something is causal or merely correlational might be the biggest part of my job as a young economist - and actually nailing down causality is really hard to do. So consider that yet another (extremely large) item on the why-(social)-science-is-hard list. We would also benefit from more media highlighting the differences between causal and correlational work - both are very important, but should have different policy implications, but they're often treated as one and the same in newspaper or online articles about research. Kudos overall, though, to FiveThirtyEight for a detailed but readable piece on the challenges of doing science currently (and how far we've come at doing better science - we've got a ways to go, but I'm optimistic that a great deal of progress has already been made).

On a lighter note (and not to be outdone), here's what might be my new favorite time-waster website: bad data presentation from WTF Visualization. Seems like the creators of these awful graphics need to read some Tufte

Make science, not (worm) wars

Make science, not (worm) wars

I have no interest in opening the can of - well, you know - that has taken the development economics twitterverse by storm this week. Without getting into the relative merits of the original study and the replication, though, I think there are lessons that the social science community can and should take away. There's a lot going on here - many of these sub-sections will likely be the topics of further posts, but this is a nice setting to discuss all of them together. More on the scientific method, replications, re-analysis, fixing problems, and science in the media after the break.

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