Parametric tests used in statistics require to specify certain conditions or assumptions to be met in order to make decisions and estimates about population parameters using sample data. The reliability of a parametric test depends heavily on the validity of the assumptions. In addition, parametric tests require at least interval scaled measurements. As you can easily imagine that restricting the data measurement level to interval scales creates a problem since the world, we live in, is not just surrounded by variables that are measured in interval scale.

A non-parametric statistical test does not specify certain conditions or assumptions to be met about population parameters using sample data. The assumptions used in non-parametric tests are that the observations in the sample are independent and the variable under study has underlying continuity. However, these assumptions are fewer and weaker than those used in parametric tests.