Non-Parametric Test

                                                                      Non-Parametric Test

Non parametric do not assume that the data is normally distributed. The only non-parametric test you are likely to come across in elementary statistics is the chi-square test. However, there are several others. For example: the Kruskal Willis test is the non-parametric alternative to the One way ANOVA and the Mann Whitney is the non-parametric alternative to the two sample t test.

The main nonparametric tests are:

Ø Sign test : Use this test to estimate the median of a population and compare it to a reference value or target value.

Ø Wilcoxon signed rank test:  With this test, you also estimate the population median and compare it to a reference/target value. However, the test assumes our data comes from a symmetric distribution.  

Ø Friedman test. This test is used to test for differences between groups with ordinal dependent variables. It can also be used for continuous data if the one-way ANOVA with repeated measures is inappropriate.

Ø Kruskal-Wallis test. Use this test instead of a one-way ANOVA to find out if two or more medians are different. Ranks of the data points are used for the calculations, rather than the data points themselves.

Ø Mann-Whitney test. Use this test to compare differences between two independent groups when dependent variables are either ordinal or continuous.

Ø Mood’s Median test. Use this test instead of the sign test when you have two independent samples.

Ø Spearman Rank Correlation  :Use when you want to find a correlation between two sets of data.