(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Jonathan Falk points me to a paper published in one of the tabloids; he had skepticism about its broad claims. I took a look at the paper, noticed a few goofy things about it (for example, “Our data also indicate a shift toward more complex societies over time in a manner that lends support to the idea of a driving force behind the evolution of increasing complexity”), and wrote back to him: “I’m too exhausted to even bother mocking this on the blog.”
Falk replied:
Well, you’d need about 30 co-bloggers to even up with the number of authors.
Actually 53, but who’s counting?
The funny thing is, it’s not like this paper is so horrible. It’s a million times better than the ovulation-and-voting paper, the beauty-and-sex-ratio paper, the ages-ending-in-9 paper, etc. It’s just an innocent little data analysis attached to some over-the-top bravado (“The approach that we have taken in this paper can be used to resolve other long-standing controversies in the study of human societies” etc etc etc). It would be ok as a final project in a social science data analysis class: you’d say the students got a lot by grappling with the data and also they learned a couple of new statistical techniques along the way. OK, they overclaimed, but ya gotta start somewhere, and there’s no harm in theorizing, as this can inform further work. To me, the most interesting of their data-based claims was this: “many trajectories exhibit long periods of stasis or gradual, slow change interspersed with sudden large increases in the measure of social complexity over a relatively short time span. This pattern is consistent with a punctuational model of social evolution, in which the evolution of larger polities requires a relatively rapid change in sociopolitical organization, including the development of new governing institutions and social roles, to be stable.” I don’t really know how seriously to take this, as I could imagine the apparent jumps being the result of data problems, but it’s an interesting thought.
There’s a problem when this sort of half-baked work gets placed in the tabloids and goes through the hype machine. On the plus side, it seems that the hype machine isn’t what it used to be: Tyler Cowen posted on this article today (I guess that’s how Falk heard about it), and the few comments were uniformly skeptical.
Again: I’m not saying this sort of data analysis and speculation shouldn’t be done—who knows what can be learned from this sort of thing, it’s worth a try! I think one problem with the tabloid model of science publication is that it incentivizes big claims.
The post Can’t keep up with the flood of gobbledygook appeared first on Statistical Modeling, Causal Inference, and Social Science.
Please comment on the article here: Statistical Modeling, Causal Inference, and Social Science
