Though they are less precise than other statistics, non-parametric statistics are useful. You will find yourself faced with small samples, populations that are obviously not normal, and data that is not cardinal. At those times, you can still make inferences about populations from samples by using non-parametric statistics.
Non-parametric statistical methods are also useful because they can often be used without a computer, or even a calculator. The Mann-Whitney U, and the t-test for the difference of sample means, test the same thing. You can usually perform the U-test without any computational help, while performing a t-test without at least a good calculator can take a lot of time. Similarly, the Wilcoxon Signed Ranks test and Spearman's Rank Correlation are easy to compute once the data has been carefully ranked. Though you should proceed on to the parametric statistics when you have access to a computer or calculator, in a pinch you can use non-parametric methods for a rough estimate.
Notice that each different non-parametric test has its own table. When your data is not cardinal, or your populations are not normal, the sampling distributions of each statistic is different. The common distributions, the t, the χ2, and the F, cannot be used.
Non-parametric statistics have their place. They do not require that we know as much about the population, or that the data measure as much about the observations. Even though they are less precise, they are often very useful.
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