The choice of significance level is one of the key steps in hypothesis testing. The proper choice requires assessment of the risks associated with Type I, Type II, and Type III statistical errors and the determination of their relative seriousness. It also includes development of criterion for the management of these risks. In this paper we review some basic concepts regarding statistical errors and hypothesis testing and outline a procedure for assessing risks associated with statistical errors which may be applicable to many research and application situations. A reasonable risk management criterion may be to select as the optimal significance level that Type I error rate that minimizes the weighted average risk associated with losses due to Type I, Type II, and Type III statistical errors.
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Carmer et al. (1988) studied this question.
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