Andrew Gelman, PhD - Election Forecasting

How is statistics used to predict elections? Andrew and Rafa discuss the U.S. 2020 Election and the role of the electoral college, polls, mail-in ballots and voter data in forecasting results and post-election outcomes. Andrew Gelman, PhD is a professor of statistics and political science at Columbia University. He is one of the go-to statisticians for the New York Times and author of perhaps the most popular statistics blog: Statistical Modeling, Causal Inference, and Social Science. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. Books he has authored and co-authored include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, Data Analysis Using Regression and Multilevel/Hierarchical Models, Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do, A Quantitative Tour of the Social Sciences, and Regression and Other Stories. Our Data Science Zoominars feature interactive conversation with data science experts and a Q+A session moderated by Rafael A. Irizarry, PhD, Chair, Department of Data Science at Dana-Farber Cancer Institute.