In hypothesis tests, analysts are usually concerned with the values of parameters, such as means or variances. To undertake such tests, analysts have had to make assumptions about the distribution of the population underlying the sample from which test statistics are derived. Given either of these qualities, the tests can be described as parametric tests.
All hypotheses tests that have been considered in this section are parametric tests.
For example, an F-test relies on two assumptions:
The F-test is concerned with the difference between the variance of the two populations. Variance is a parameter of a normal distribution. Therefore, the F-test is a parametric test.
There are other types of hypothesis tests, which may not involve a population parameter or much in the way of assumptions about the population distribution underlying a parameter. Such tests are nonparametric tests.
Nonparametric tests have different characteristics:
Nonparametric tests are normally used in three cases:
In general, parametric tests are preferred where they are applicable. They have stricter assumptions that, when met, allow for stronger conclusions. However, nonparametric tests have broader applicability and, while not as precise, do add to your understanding of phenomena, particularly when no parametric tests can be effectively used.
|achu: non parametric tests weaker, but require fewer distributional assumptions. in some cases they thus can be useful.|
|AUAU: can anyone gives some example for non-parametric tests|
|jpducros: Runs tests (which examine the pattern of successive increases or decreases in a random variable) and rank correlation tests (which examine the relation between a random variable's relative numerical periods) are examples of non parametric tests.|
|Shaan23: kaplan meier test.|