Nonparametric statistics
Nonparametric Statistics
Nonparametric statistics (pronunciation: non-pa-ra-me-tric sta-tis-tics) is a branch of statistics that does not make any assumptions about the probability distribution of the variables being assessed. The term "nonparametric" (etymology: Greek, non meaning "not" and parametrikos meaning "measurable") does not imply that such statistics have no parameters, but that the number and nature of the parameters are flexible and not fixed in advance.
Overview
Nonparametric statistics includes both descriptive and inferential statistical methods. Descriptive nonparametric methods involve summarizing and presenting data in a convenient and informative way, while inferential nonparametric methods involve making conclusions or predictions about a population based on data from a sample.
Methods
Common nonparametric methods include the Mann-Whitney U test, the Kruskal-Wallis test, and the Spearman's rank correlation coefficient. These methods are often used when the assumptions of parametric methods are not met, or when dealing with ordinal data or data on an interval scale.
Advantages and Disadvantages
Nonparametric statistics have several advantages. They are more robust than parametric statistics, meaning they are less affected by outliers or extreme values. They can also be used with small sample sizes and with data that are not normally distributed.
However, nonparametric statistics also have some disadvantages. They can be less powerful than parametric statistics, meaning they may not detect a significant effect or difference when one exists. They also do not provide as much information about the data as parametric statistics do.
Related Terms
- Parametric statistics
- Descriptive statistics
- Inferential statistics
- Probability distribution
- Mann-Whitney U test
- Kruskal-Wallis test
- Spearman's rank correlation coefficient
External links
- Medical encyclopedia article on Nonparametric statistics
- Wikipedia's article - Nonparametric statistics
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