Last week I discovered (for myself anyway) the Social Science Statistics Blog, which is run by a group of hot-shot graduate students from various departments at Harvard, including Health Policy, Government, Political Science, Sociology, and Statistics. Most of them appear to have an affiliation with the Institute for Quantitative Social Science. Their posts have a humor, edginess, and candor that is hard not to like--will they be this interesting when they all get academic jobs? I hope so.
Here is the introduction to an interesting post by Jens Hainmueller (government), entitled The Role of Sample Size and Unobserved Heterogeneity in Causal Inference:
Here is a question for you: Imagine you are asked to conduct an observational study to estimate the effect of wearing a helmet on the risk of death in motorcycle crashes. You have to choose one of two different data-sets for this study: Either a large, rather heterogeneous sample of crashes (these happened on different roads, at different speeds, etc.) or a smaller, more homogeneous sample of crashes (let's say they all occurred on the same road). Your goal is to unearth a trustworthy estimate of the treatment effect that is as close as possible to the `truth', i.e. the effect estimate obtained from an (unethical) experimental study on the same subject. Which sample do you prefer?
See here for the rest of Jens' post.