Gelman is the Higgins Professor of Statistics and Professor of Political Science and the Director of the Applied Statistics Center at Columbia University. He is a major contributor to statistical philosophy and methods especially in
Bayesian statistics and
hierarchical models. He is one of the leaders of the development of the statistical programming framework
Stan.
Perspective on Statistical Inference and Hypothesis Testing Gelman's approach to statistical inference emphasizes studying variation and the associations between data, rather than searching for
statistical significance. Gelman says his approach to hypothesis testing is "(nearly) the opposite of the conventional view" of what is typical for statistical inference. While the standard approach may be seen as having the goal of rejecting a null hypothesis, Gelman argues that you can't learn much from a rejection. On the other hand, a non-rejection tells you something: "[it] tells you that your study is noisy, that you don't have enough information in your study to identify what you care about—even if the study is done perfectly, even if measurements are unbiased and your sample is representative of your population, etc. That can be some useful knowledge, it means you're off the hook trying to explain some pattern that might just be noise." Gelman also works within the context of larger confirmationist and falsificationist paradigms of science. Gelman's approach to statistical inference is a major recurring theme of his work. == Popular press ==