Andrew Gelman, Department of Statistics, Columbia University
We’ve been hearing a lot about “data” recently, but data are generally a means to an end, with the goal being to learn about some population of interest. How do we generalize from sample to population? The process seems a bit mysterious, especially given that our samples are far from random (unless you count a sample of 24 psychology undergraduates and 100 Mechanical Turk participants as a random sample). Nonetheless we can use data to learn about populations of interest, in part using Bayesian reasoning. We discuss principles, methods, and open problems, and issues of sample size, effect size, and interactions, in the context of examples in political science and psychology.
The talk will be at 368 ISR located at 426 Thompson St.
Some relevant recent articles are here:
 Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups. American Journal of Political Science. (Yair Ghitza and Andrew Gelman)
 Forecasting elections with non-representative polls. International Journal of Forecasting. (Wei Wang, David Rothschild, Sharad Goel, Andrew Gelman)
The mythical swing voter. (David Rothschild, Sharad Goel, Andrew Gelman, Douglas Rivers)
Statistical graphics for survey weights. (Susanna Makela, Yajuan Si, and Andrew Gelman):
Bayesian nonparametric weighted sampling inference. (Yajuan Si, Natesh Pillai, and Andrew Gelman):
And some relevant recent notes are here:
President of American Association of Buggy-Whip Manufacturers takes a strong stand against internal combustion engine, argues that the so-called “automobile” has “little grounding in theory” and that “results can vary widely based on the particular fuel that is used”