Question

How many types of tests are considered non-parametric data and briefly explain each

Answer 1

What is a Non Parametric Test?
A non parametric test (sometimes called a distribution free test) does not assume anything about the underlying distribution (for example, that the data comes from a normal distribution). That’s compared to parametric test, which makes assumptions about a population’s parameters (for example, the mean or standard deviation); When the word “non parametric” is used in stats, it doesn’t quite mean that you know nothing about the population. It usually means that you know the population data does not have a normal distribution.
For example, one assumption for the one way ANOVA is that the data comes from a normal distribution. If your data isn’t normally distributed, you can’t run an ANOVA, but you can run the nonparametric alternative—the Kruskal-Wallis test. If at all possible, you should us parametric tests, as they tend to be more accurate. Parametric tests have greater statistical power, which means they are likely to find a true significant effect. Use nonparametric tests only if you have to (i.e. you know that assumptions like normality are being violated). Nonparametric tests can perform well with non-normal continuous data if you have a sufficiently large sample size (generally 15-20 items in each group).

Types of Nonparametric Tests:
When the word “parametric” is used in stats, it usually means tests like ANOVA or a t test. Those tests both assume that the population data has a normal distribution. Non parametric do not assume that the data is normally distributed. The only non parametric test you are likely to come across in elementary stats 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:
(i) 1-sample sign test: Use this test to estimate the median of a population and compare it to a reference value or target value.
(ii) 1-sample 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 your data comes from a symmetric distribution (like the Cauchy distribution or uniform distribution).
(iii) 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 (i.e. some assumption has been violated).
(iv) Goodman Kruska’s Gamma: a test of association for ranked variables.
(v) 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.
(vi) The Mann-Kendall Trend Test: This test looks for trends in time-series data.
(vii) Mann-Whitney test: Use this test to compare differences between two independent groups when dependent variables are either ordinal or continuous.
(viii) Mood’s Median test: Use this test instead of the sign test when you have two independent samples.
(ix) Spearman Rank Correlation: Use when you want to find a correlation between two sets of data.