Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Issues of multiplicity in testing are increasingly being encountered in a wide range of disciplines, as the growing complexity of data allows for consideration of a multitude of possible hypotheses (e.g., does gene xyz affect condition abc). Failure to properly adjust for multiplicities is being blamed for the apparently increasing lack of reproducibility in science. The main purpose of this presentation is to review the different types of multiplicities that are encountered, and to discuss the general approaches to dealing with them that are being adopted by frequentists and Bayesians (with more emphasis on the latter). A secondary goal of the talk is to discuss the role that modern multiplicity adjustments, such as FDR, might play in Bayesian multiple testing analysis, and to explore if some unification of multiple testing methodologies occurs.