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Parametric vs. non-parametric tests
Explanations > Social Research > Analysis > Parametric vs. non-parametric tests
There are two types of test data and consequently different types of analysis. As the table below shows, parametric data has an underlying normal distribution which allows for more conclusions to be drawn as the shape can be mathematically described. Anything else is non-parametric.
| Parametric | Non-parametric | |
| Assumed distribution | Normal | Any |
| Assumed variance | Homogeneous | Any |
| Typical data | Ratio or Interval | Ordinal or Nominal |
| Data set relationships | Independent | Any |
| Usual central measure | Mean | Median |
| Benefits | Can draw more conclusions | Simplicity; Less affected by outliers |
| Tests | ||
| Choosing | Choosing parametric test | Choosing a non-parametric test |
| Correlation test | Pearson | Spearman |
| Independent measures, 2 groups | Independent-measures t-test | Mann-Whitney test |
| Independent measures, >2 groups | One-way, independent-measures ANOVA | Kruskal-Wallis test |
| Repeated measures, 2 conditions | Matched-pair t-test |
Wilcoxon test |
| Repeated measures, >2 conditions | One-way, repeated measures ANOVA | Friedman's test |
As the table shows, there are different tests for parametric and non-parametric data.
See also
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