At the Center for Economic and Social Research, Dauterive Hall (VPD), room 110, 635 Downey Way, Los Angeles:
The study of American politics as a window into understanding uncertainty in science
Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University
We begin by discussing recent American elections in the context of political polarization, and we consider similarities and differences with European politics. We then discuss statistical challenges in the measurement of public opinion: inference from opinion polls with declining response rates has much in common with challenges in big-data analytics. From here we move to the recent replication crisis in science, and we argue that Bayesian methods are well suited to resolve some of these problems, if researchers can move away from inappropriate demands for certainty. We illustrate with examples in many different fields of research, our own and others’.
Some background reading:
19 things we learned from the 2016 election (with Julia Azari), http://www.stat.columbia.edu/~gelman/research/published/what_learned_in_2016_5.pdf
The mythical swing voter (with Sharad Goel, Doug Rivers, and David Rothschild). http://www.stat.columbia.edu/~gelman/research/published/swingers.pdf
The failure of null hypothesis significance testing when studying incremental changes, and what to do about it. http://www.stat.columbia.edu/~gelman/research/published/incrementalism_3.pdf
Honesty and transparency are not enough. http://www.stat.columbia.edu/~gelman/research/published/ChanceEthics14.pdf
The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective. http://www.stat.columbia.edu/~gelman/research/published/bayes_management.pdf
The talk will mostly be about statistics, not political science, but it’s good to have a substantive home base when talking about methods.