An assumption for many statistical tests, such as the t-tests and ANOVA, is that the data are normally distributed. When this normality assumption is suspect or cannot be met, there are alternative techniques for analyzing the data.

Statistical techniques based on an assumption that data are distributed according to some parameterized distribution (such as the normal distribution) are referred to as parametric analyses. Statistical techniques that do not rely on this assumption are called nonparametric procedures. This chapter illustrates several nonparametric techniques. One nonparametric procedure, Spearman's rank correlation, has been discussed earlier in Chapter 12.


A nonparametric test can be used to compare two independent groups when you cannot make the assumptions associated with the t-test. Using a nonparametric test is also a useful technique if you do not have exact data values for the observations but you do have order ...

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