Non-parametric statistical procedures rely on no or few assumptions about the shape of the distribution of the measured characteristic of interest in the underlying population. Commonly used non-parametric tests are Wilcoxon, Mann-Whitney and Kruksal-Wallis. Reasons for using such tests may be that the sample size is very small, the data are ordinal/ranked in nature, or that there is a skewed distribution (e.g. survival, income) with extreme outliers (long ‘tail’) and a summary statistic such as median may be of more value than a mean. Non-parametric tests generally have less power (i.e. will require larger sample sizes) than the corresponding parametric tests if the data are truly normal, and interpretation of the results of such procedures can also be more difficult. Generally, non-parametric methods are of limited use for economic evaluation, where the focus is more on estimation (to support decision-making) than on hypothesis testing. Nevertheless, bootstrapping is a useful non-parametric technique, and direct use of (Kaplan-Meier) survival data from source studies to estate survival of a modelled cohort may also be considered to be non-parametric, in contrast to the use of parametric functions.

How to cite: Non-Parametric (Tests) [online]. (2016). York; York Health Economics Consortium; 2016.


Contact us today if you would like to be kept updated with our latest training courses: