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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.

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