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Many critics of nonparametric tests have pointed out some major drawbacks of the tests: (1) they are usually neither as powerful nor as efficient as the parametric tests; (2) they are not as precise or as accurate as parametric tests in many cases (e.g., ranking tests with a large number of ties); (3) they might lead to erroneous decisions about rejecting or not rejecting the null hypothesis because of lack of precision in the test; (4) many of these tests utilize data inadequately in the analysis because they transform observed values into ranks and groups; and (5) the sampling distribution and distribution tables for nonparametric statistics are too numerous, are often cumbersome, and are limited to small sample sizes.
Nonparametric Statistics - Investopedia
In statistical inference, or hypothesis testing, the traditional tests are called parametric tests because they depend on the specification of a probability distribution (such as the normal) except for a set of free parameters. Parametric tests are said to depend on distributional assumptions. Nonparametric tests, on the other hand, do not require distributional assumptions. Even if the data are distributed normally, nonparametric methods are often almost as powerful as parametric methods.
The course will integrate exploratory data analysis and nonparametric statistical inference. The emphasis will be on analysis and interpretation of data. You should be familiar with summary statistics, graphs, hypothesis tests, confidence intervals and the basics of statistical inference.