This entry provides an overview of experimental design using a Bayesian decision-theoretic framework. Scientific experimentation requires decisions about how an experiment will be conducted and analyzed. Such decisions depend on the goals and purpose of the experiment, but certain choices may be restricted by available resources and ethical considerations. Prior information may be available from earlier experiments or from conjectures which motivate the investigation. The Bayesian approach provides a coherent framework where prior information and uncertainties regarding unknown quantities can be combined to find an experimental design that optimizes the goals of the experiment.
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To Appear in the International Encyclopedia of Socal and Behaviorial Sciences Volume 2.1