Nonparametric Method

A nonparametric method is a statistical technique that does not require assumptions about the underlying distribution of the data. This makes them more flexible than parametric methods, which require assumptions about the distribution of the data in order to make inferences.

Nonparametric methods are often used in situations where the data is not normally distributed or where there is a small sample size. They can also be used to compare two or more groups of data when the distributions of the data are not known.

There are a number of different nonparametric methods available, each of which is suited to different types of data and problems. Some of the most common nonparametric methods include:

* The Mann-Whitney U test is used to compare two independent groups of data.
* The Kruskal-Wallis test is used to compare more than two independent groups of data.
* The Wilcoxon signed-rank test is used to compare two dependent groups of data.
* The Friedman test is used to compare more than two dependent groups of data.

Nonparametric methods are a powerful tool for data analysis and can be used to solve a variety of problems. However, it is important to choose the right method for the data and the problem at hand.