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In this chapter we describe procedures called nonparametric and distribution-free methods, and e usually make no assumptions about the distribution of the underlying population other than that it is continuous These procedures have actual level of signi cance or con dence level 100(1 )% for many different types of distributions These procedures have considerable appeal One of their advantages is that the data need not be quantitative but can be categorical (such as yes or no, defective or nondefective) or rank data Another advantage is that nonparametric procedures are usually very quick and easy to perform The procedures described in this chapter are competitors of the parametric t- and F-procedures described earlier Consequently, it is important to compare the performance of both parametric and nonparametric methods under the assumptions of both normal and nonnormal populations In general, nonparametric procedures do not utilize all the information provided by the sample.

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As a result, a nonparametric procedure will be less ef cient than the corresponding parametric procedure when the underlying population is normal This loss of ef ciency is re ected by a requirement of a larger sample size for the nonparametric procedure than would be required by the parametric procedure in order to achieve the same power On the other hand, this loss of ef ciency is usually not large, and often the difference in sample size is very small When the underlying distributions are not close to normal, nonparametric methods have much to offer They often provide considerable improvement over the normal-theory parametric methods Generally, if both parametric and nonparametric methods are applicable to a particular problem, we should use the more ef cient parametric procedure However, the assumptions for the parametric method may be dif cult or impossible to justify.

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