Clinical studies in drug abuse research and other areas in psychiatry provide some of the most challenging data for analysis and hypothesis testing. Researchers ’ reliance on subjects ’ self-reports, the need to assess illegal behaviors, and high rates of participant attrition are just some of the common sources of noise accompanying the treatment signal. To this list one can add the common occurrence of data that may be less than ideally distributed. To ignore the distribution of the observed data or to blindly use methods based on untenable assumptions about the characteristics of the data is to court statistical trouble that may lead to invalid estimates of effects and p values. In this paper we focus on a particular problem—too many zero values in the data. This phenomenon is foun
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