Introduction to non-parametric methods

Introduction to non-parametric methods

  • 12/01/2022
    1:00 pm - 2:30 pm

Course details

statistics seminar | level: intermediate | register now
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affiliation: Ghent University


This seminar is about the essentials of non-parametric methods. Non-parametric tests are more suitable for data that come from skewed distributions or have a discrete or ordinal scale. They are also the method of choice for small sample sized data.

The first section is about non-parametric hypothesis testing wherein methods such as the sign test (one-sample), Wilcoxon Rank Sum Test (for 2 samples) and the Kruskal–Wallis Test (more than 2 samples) and presented. The aforementioned tests for 2 or more sample all assume that the observations are independently sampled. For dependent samples, alternatives such as the Wilcoxon Signed Rank Test ( for 2 samples) and the Friedman test (for more than 2 samples) should be considered.

The second section is about non-parametric measures for gauging the strength of associations between 2 variables with special attention to the situation where the two variables can be all continuous, one continuous and one categorical and the case where both are categorical.

Finally, section 3 is about non-parametric regression where the linearity assumption which characterizes linear models is relaxed. Different types of non-parametric regression models will be explained with demonstrations of how to fit and assess the models.

The examples will be demonstrated using RStudio and all codes will be provided


basic statistics

Background readings






Emmanuel Abatih

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