Last week I came across an article I wish I'd found a year or two ago: "Glossary for econometrics and epidemiology" (PDF from JSTOR, ungated version here) by Gunasekara, Carter, and Blakely. Statistics is to some extent a common language for the social sciences, but there are also big variations in language that can cause problems when students and scholars try to read literature from outside their fields. I first learned epidemiology and biostatistics at a school of public health, and now this year I'm taking econometrics from an economist, as well as other classes that draw heavily on the economics literature.
Friends in my economics-centered program have asked me "what's biostatistics?" Likewise, public health friends have asked "what's econometrics?" (or just commented that it's a silly name). In reality both fields use many of the same techniques with different language and emphases. The Gunasekara, Carter, and Blakely glossary linked above covers the following terms, amongst others:
- confounding
- endogeneity and endogenous variables
- exogenous variables
- simultaneity, social drift, social selection, and reverse causality
- instrumental variables
- intermediate or mediating variables
- multicollinearity
- omitted variable bias
- unobserved heterogeneity
If you've only studied econometrics or biostatistics, chances are at least some of these terms will be new to you, even though most have roughly equivalent forms in the other field.
Outside of differing language, another difference is in the frequency with which techniques are used. For instance, instrumental variables seem (to me) to be under-used in public health / epidemiology applications. I took four terms of biostatistics at Johns Hopkins and don't recall instrumental variables being mentioned even once! On the other hand, economists just recently discovered randomized trials. (Now they're more widely used) .
But even within a given statistical technique there are important differences. You might think that all social scientists doing, say, multiple linear regression to analyze observational data or critiquing the results of randomized controlled trials would use the same language. In my experience they not only use different vocabulary for the same things, they also emphasize different things. About a third to half of my epidemiology coursework involved establishing causal models (often with directed acyclic graphs) in order to understand which confounding variables to control for in a regression, whereas in econometrics we (very!) briefly discussed how to decide which covariates might cause omitted variable bias. These discussions were basically about the same thing, but they differed in terms of language and in terms of emphasis.
I think an understanding of how and why researchers from different fields talk about things differently helps you to understand the sociology and motivations of each field. This is all related to what Marc Bellemare calls the ongoing "methodological convergence in the social sciences." As research becomes more interdisciplinary -- and as any applications of research are much more likely to require interdisciplinary knowledge -- understanding how researchers trained in different academic schools think and talk will become increasingly important.