Bayesian inference for A/B testing: Lauren Kennedy and I speak at the NYC Women in Machine Learning and Data Science meetup tomorrow (Tues 27 Mar) 7pm

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Here it is:

Bayesian inference for A/B testing

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University
Lauren Kennedy, Columbia Population Research Center, Columbia University

Suppose we want to use empirical data to compare two or more decisions or treatment options. Classical statistical methods based on statistical significance and p-values break down in the context of incremental improvement: that is, when there is a stream of innovations, each only slightly better (or possibly slightly worse) than what came before. In contrast, a Bayesian approach is ideally suited to decision making under uncertainty. We discuss the implications for applied statistics and code up some of these models in R and Stan, based on a case study by Bob Carpenter.

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Tags: Bayesian statistics, Causal Inference, Decision Theory, economics, Multilevel Modeling, STAN, Statistical computing