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Choosing a non-parametric test

 

Explanations > Social ResearchAnalysis > Choosing a non-parametric test

Choosing the test | Discussion | See also

 

Choosing the test

Use the table below to choose the test. See below for further details.

 

How many separate samples?

1

How many scores for each subject?

1

How many measurement categories?

2

Binomial test

2+

Chi-square test for goodness of fit

2

Can difference scores be ranked?

Y

Wilcoxon test

N

Sign test

2

Matched samples? (N = independent)

Y

Can difference scores be ranked?

Y

Wilcoxon test

N

Sign test

N

Can scores be ranked with few tied values? (independent samples only)

Y

Median test

Y

Mann-Whitney test
N Chi-square test for independence
>2

Can scores be ranked with few tied values? (independent samples only)

Y

Median test

N

Chi-square test for independence
N Krushkal-Wallas test

 

Discussion

Non-parametric tests do not assume an underlying Normal (bell-shaped) distribution.

There are two general situations when non-parametric tests are used:

  1. Data is nominal or ordinal (where means and variance cannot be calculated).
  2. The data does not satisfy other assumptions underlying parametric tests.

See also

Parametric vs. non-parametric tests

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