Split-Sample Strategies for Avoiding False Discoveries
Department of Agricultural & Resource Economics
University of California, Berkeley
Friday, November 2, 2018
Taylor-Hibbard Seminar Room (Rm103)
12:00 pm-1:15 pm
Preanalysis plans (PAPs) have become an important tool for limiting false discoveries in field experiments. We evaluate the properties of an alternate approach which splits the data into two samples: An exploratory sample and a confirmation sample. When hypotheses are homogeneous, we describe an improved split-sample approach that achieves 90% of the rejections of the optimal PAP without requiring preregistration or constraints on specification search in the exploratory sample. When hypotheses are heterogeneous in priors or intrinsic interest, we find that a hybrid approach which prespecifies hypotheses with high weights and priors and uses a split-sample approach to test additional hypotheses can have power gains over any pure PAP. We assess this approach using the community-driven development (CDD) application from Casey et al. (2012) and find that the use of a hybrid split-sample approach would have generated qualitatively different conclusions.