Previous classical and Bayesian formulations of compliance assessment rules based on a nonparametric approach are compared with formulations based on the assumption that compliance assessment data have been randomly drawn from a normal population with unknown mean and variance. Graphs of parametric (Bayesian) "Confidence of Compliance" curves are presented. With one exception it is concluded that compliance rules based on a nonparametric approach are the more robust, as their formulation does not depend on any assumption as to the nature of the parent distribution and because rules devised under either approach are generally similar. The exception occurs for rules based on minimizing the consumer's risk (i.e., environment's risk) when a large number of samples are to hand and goodness-of-fit tests give strong grounds for the assumption of a normal parent. In that case the parametric compliance rule--either Bayesian or classical--becomes rather less strict.