Statistical inference and meta-analysis

Statistical inference is a common feature of meta-analysis. Statistical inference depends on a formal model that accurately characterizes certain key features of how the studies to be summarized were generated. The implications of this requirement are discussed and questions are raised about the credibility of confidence intervals and hypothesis tests routinely reported.

Richard BerkEmail:

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Richard Berk   is a professor in the Departments of Criminology and Statistics at the University of Pennsylvania. He is an elected fellow of the American Statistical Association, the American Association for the Advancement of Science, and the Academy of Experimental Criminology. His research interests include statistical learning procedures and applied statistics more generally. He has published extensively in program evaluation, criminal justice, environmental research, and applied statistics. Professor Berk’s most recent book is Regression Analysis: A Constructive Critique (Sage Publications, 2003).