eChapter Name: Non-parametric Tests
9789358872415
eBook Name: QUESTION BANK ON STATISTICS
by Rakesh Goel
A non parametric test (sometimes called a distribution free test) is a statistical analysis method that does not assume the population data belongs to some prescribed distribution which is determined by some parameters. That means, a non-parametric test can be defined as a test that is used in statistical analysis when the data under consideration does not belong to a parameterized family of distributions. When the data does not meet the requirements to perform a parametric test, a non-parametric test is used to analyze it. The non-parametric test 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 means that population data does not have a normal distribution.
If at all possible, we 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. Nonparametric tests can perform well with non-normal continuous data if the data of the sample are sufficiently large. Non parametric do not assume that the data is normally distributed. The only non parametric test we are likely to come across in elementary stats is the Chi-square test. In nonparametric tests have several advantages as compared to parametric tests. It includes:
• More statistical power when assumptions for the parametric tests have been violated.
• Fewer assumptions, i.e. the assumption of normality don’t apply.
• Small sample sizes are acceptable.
• It is used for all data types, including nominal data, rank data or ordinal data
• Knowledge of the population is not required to conduct this test.
• Nonparametric tests are valid when our sample size is small and our data are potentially non-normal.