Is Rigor Contagious?
Much of the theory and practice of statistics and econometrics is characterized by a toxic mixture of rigor and sloppiness. Methods are justified based on seemingly pure principles that can’t survive reality. Examples of these principles include random sampling, unbiased estimation, hypothesis testing, Bayesian inference, and causal identification. Examples of uncomfortable reality include nonresponse, varying effects, researcher degrees of freedom, actual prior information, and the desire for external validity. We discuss a series of scenarios where researchers naively think that rigor in one part of their design and analysis will assure rigor on their larger conclusions, and then we discuss possible hierarchical Bayesian solutions in which the load of rigor is more evenly balanced across the chain of scientific reasoning.
The talk (for the Sustainable Development seminar) will be Mon 27 Feb, 4:15-5:45, in room 801 International Affairs Building at Columbia.