A parametric test specifies certain conditions about the distribution of responses in the
population from which the research sample was drawn. The meaningfulness of the results
of a parametric test depends on the validity of these assumptions. A nonparametric test is
based on a model that specifies very general conditions and none regarding the specific
form of the distribution from which the sample was drawn. Hence nonparametric tests are
also known as distribution free tests. Certain assumptions are associated with most
nonparametric statistical tests, but these are fewer and weaker than those of parametric
tests
