Question

explain some distinguishing feature of non parametric tests?

Answer 1

Answer:

Features of Non Parametric Test

From what has been stated in respect of important non-parametric tests, we can say that these tests share in main the following characteristics or features:

1. They do not suppose any particular distribution and the consequential assumptions.
2. They are rather quick and easy to use i.e., they do not require laborious computations since in many cases the observations are replaced by their rank order and in many others we simply use signs.
3. They are often not as efficient or ‘sharp’ as tests of significance or the parametric tests. An interval estimate with 95% confidence may be twice as large with the use of nonparametric tests as with regular standard methods. The reason being that these tests do not use all the available information but rather use groupings or rankings and the price we pay is a loss in efficiency. In fact, when we use non-parametric tests, we make a trade-off: we loose sharpness in estimating intervals, but we gain the ability to use less information and to calculate faster.
4. When our measurements are not as accurate as is necessary for standard tests of significance, then non-parametric methods come to our rescue which can be used fairly satisfactorily.
5. Parametric tests cannot apply to ordinal or nominal scale data but non-parametric tests do not suffer from any such limitation.
6. The parametric tests of difference like ‘t’ or ‘F’ make assumption about the homogeneity of the variances whereas this is not necessary for non-parametric tests of difference.

Comparision of parametric and non parametric

Parametric tests:

·Require assumptions about population characteristics: normality of the underlying distribution, homogeneity of variance, known mean / variance.

·Examples: F, z, t tests

Nonparametric tests:

·Do not require assumptions about population characteristics.

·Can be used with very skewed distributions or when the population variance is not homogeneous.

·Can be used with ordinal or nominal data.

·Examples: Chi-square, Wilcoxon, and Kruskal-Wallis tests

Nonparametric tests are less powerful than parametric tests, so we don’t use them when parametric tests are appropriate.  But if the assumptions of parametric tests are violated, we use nonparametric tests.