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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

Data design

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