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Split-Sample Strategies for Avoiding False Discoveries

Seminar: Neyman Seminar | October 11 | 4-5 p.m. | 1011 Evans Hall


Michael Anderson, University of California, Berkeley

Department of Statistics


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.


(510) 642-2781