## Non-Parametric Tests: Introduction

Non-parametric tests are experiments that do not require the underlying population for assumptions. It does not rely on data referring to any particular parametric group of probability distributions. Non-parametric methods are also called distribution-free tests since they do not have any underlying population.

**Non-parametric statistics are helpful when**

- Inferences must be made on categorical or ordinal data
- The assumption of normality is not appropriate
- The sample size is small

**Advantages of Non-Parametric Methods**

- Easy application (does not even need a calculator in many cases)
- It can serve as a quick check to determine whether or not further analysis is required
- Many assumptions concerning the population of the data source can be relaxed
- Can be used to test categorical (yes/no) data
- Can be used to test ordinal (1, 2, 3) data

**Disadvantages of Non-Parametric Methods**

- Nonparametric procedures are less efficient than parametric procedures. It means that nonparametric tests require a larger sample size to have the same probability of a type-I error as the equivalent parametric procedure.
- Nonparametric procedures often discard helpful information. That is, the magnitudes of the actual data values are lost. As a result, nonparametric procedures are typically less powerful.

That is they produce conclusions that have a higher probability of being incorrect. Examples of widely used Parametric Tests: include the paired and unpaired t-test, Pearson’s product-moment correlation, Analysis of Variance (ANOVA), and multiple regression.

Do not use nonparametric procedures if parametric procedures can be used.

**Some widely used Non-Parametric Tests are:**

- Sign Test
- Runs Test
- Wilcoxon Signed Rank Test
- Wilcoxon Rank Sum Test
- Spearman’s Rank Correlation
- Kruskal Wallis Test
- Chi-Square Goodness of Fit Test

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