NON-PARAMETRIC STATISTICS

In statistics, the term non-parametric statistics refers to statistics that do not assume the data or population have any characteristic structure or parameters. For example, non-parametric statistics are suitable for examining the order of a set of students ranked by a test result. Or a non-parametric statistical test is one which does not specify any conditions about the parameter of the population from which the sample is drawn. They are also called distribution free statistics certain conditions are associated to non-parametric statistics but they are less rigid. The assumptions of the tests are the following:

1.   Variable under study should be continuous and the observation should be independent. Example- chi-square test.

2.   A non-parametric statistical test should be used only when the shape of the distribution of the population from which the sample is drawn is not known to be a normal one.

3.   The variable have been quantified on the basis of nominal measures and of ordinal measures as because non-parametric test are based upon nominal and ordinal measures. They are precise and less likely to reject a null hypothesis when it is false that is why a non-parametric statistical test is used only when parametric assumptions can’t be met.

Non-parametric tests are also referred to as distribution free tests.These tests have the obvious advantage of not requiring the assumptions of normality or the assumptions of homogeneity of variance. They compare medians rather than means and ,as a result, if the data has one or two outliers, there influence is negated.