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Most of the hypothesis-testing procedures discussed in previous chapters are based on the assumption that the random samples are selected from normal populations. Fortunately, most of these tests are still reliable when we experience slight depar-tmvs from normality, pArtimilprly whim the ssImple si7p is is r. – Traditionally, these testing procedures have been referred to as parametric methods. In this chapter, we consider a number of alternative test procedures, called nonparamet-rie or distributinti-free mpthnris, that often assume no knowledge whatsoever about the distributions of the underlying populations, except perhaps that they are continuous.
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Get Help Now!Nonparametric, or distribution-free procedures, are used with increasing fre-quency by data analysts. There are many applications in science and engineering where the data are reported as values not on a continuum but rather on an ordinal scale such that it is quite natural to assign ranks to the data. In fact, the reader may notice quite early in this chapter that the distribution-free methods described here involve an analysis of ranks. Most analysts find the computations involved in nonparametric methods to be very appealing and intuitive.
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