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Sriya, A non parametric test specifies certain conditions about the parameters of the population from which the research sample was drawn. It includes several conditions; The observations must be independent and must be drawn from normally distributed populations. The populations must have the same variances and variables involved must have been measured in at least an interval scale. Most of the students are familiar with parametric test when compared to that of non parametric test. Non parametric tests do not rely on any kind of distribution and are used to test group medians. Pros of using Non parametric test over parametric test are 1. Non-parametric tests deliver accurate results even when the sample size is small. 2. Non-parametric tests are more powerful than parametric tests when the assumptions of normality have been violated. 3. They are suitable for all data types, such as nominal, ordinal, interval or the data which has outliers. You can use Non parametric test: When the outcome is an ordinal variable or a rank When there are definite outliers When the outcome has clear limits of detection

Non parametric test is used when assumptions of parametric tests are not met such as the level of measurement (e.g., interval or ratio data), normal distribution, and homogeneity of variances across groups. Some of the non parametric tests are Chi square, Mann Whitney, Wilcoxon matched pairs, Kruskal- Wallis test. Some of the benefits of using this test are Non-parametric tests make less suppositions about the information and are progressively significant to a specific circumstance. Also, the theory tried by the non-parametric test might be increasingly fitting for the exploration examination. Non-parametric strategies are accessible to treat information which are basically classificatory or downright, i.e., are estimated in an ostensible scale. Non parametric system applies to such information. Non-parametric measurable tests normally are a lot less demanding to learn and to apply than are parametric tests. Moreover, their translation frequently is more straightforward than the understanding of parametric tests.