Abstract

Three main lines of arguments are presented as a defense of randomization in experimental design. The first concerns the computational advantages of randomizing when a well-defined underlying theoretical model is not available, as is often the case in much experimentation in the medical and social sciences. The high desirability, even for the most dedicated Bayesians, of physical randomization in some special cases is stressed. The second line of argument concerns communication of methodology and results, especially in terms of concerns about bias. The third line of argument concerns the use of randomization to guarantee causal inferences, whether the inference consists of the identification of a prima facie or a genuine cause. In addition, the relation of randomization to measures of complexity and the possibility of accepting only random procedures that produce complex results are discussed.