American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



WHY WE DON'T (USUALLY) WORRY
ABOUT MULTIPLE COMPARISONS

by

Prof. Andrew Gelman
Department of Statistics
Columbia University


Abstract

The problem of multiple comparisons can disappear when viewed from a Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. These address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p-values corresponding to intervals of fixed width). Multilevel estimates make comparisons more conservative, in the sense that intervals for comparisons are less likely to include zero; as a result, those comparisons that are made with confidence are more likely to be valid.


Date: Thursday, May 15, 2008
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room
4th Floor - Room 425
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (R627).


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