Nonparametric vs. Parametric tests

Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables.

1. What is the difference between nonparametric and parametric tests?
2. What is an example of a nonparametric test?
3. Are parametric tests better than nonparametric?

What is the difference between nonparametric and parametric tests?

The key difference between parametric and nonparametric test is that the parametric test relies on statistical distributions in data whereas nonparametric do not depend on any distribution. Non-parametric does not make any assumptions and measures the central tendency with the median value.

What is an example of a nonparametric test?

A histogram is an example of a nonparametric estimate of a probability distribution.

Are parametric tests better than nonparametric?

Parametric tests usually have more statistical power than nonparametric tests. Thus, you are more likely to detect a significant effect when one truly exists.