It's About Time
Andrew Gelman is a professor of statistics and political science at Columbia University and a major contributor to statistical philosophy and methods, especially in the field of Bayesian statistics. He is also heavily involved in the development of the probabilistic programming language Stan.
Abstract
Statistical processes occur in time, but this is often not accounted for in the methods we use and the models we fit. Examples include imbalance in causal inference, generalization from A/B tests even when there is balance, sequential analysis, adjustment for pre-treatment measurements, poll aggregation, spatial and network models, chess ratings, sports analytics, and the replication crisis in science. The point of this talk is to motivate you to include time as a factor in your statistical analyses. This may change how you think about many applied problems!