Comparing means

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The following tables provide guidance to the selection of the proper parametric or non-pametric tests for a given data set.

Contents 1 Is there a difference ? 2 Sources 3 See also 4 External links

[edit] Is there a difference ?

Ordinal and numerical measures

1 group		N ≥ 30		One-sample t-test
		N < 30	Normally distributed	One-sample t-test
			Not normal	Sign test
2 groups	Independent	N ≥ 30		t-test
		N < 30	Normally distributed	t-test
			Not normal	Mann-Whitney U or Wilcoxon rank sum test
	Paired	N ≥ 30		paired t-test
		N < 30	Normally distributed	paired t-test
			Not normal	Wilcoxon signed-rank test
3 or more groups	Independent	Normally distributed	1 factor	One way anova
			≥ 2 factors	two or other anova
		Not normal		Kruskal-Wallis one-way analysis of variance by ranks
	Dependent	Normally distributed		Repeated measures anova
		Not normal		Friedman two-way analysis of variance by ranks

Nominal measures

1 group		np and n(1-p) ≥ 5	z-approximation
		np or n(1-p) < 5	binominal
2 groups	Independent	np < 5	fisher exact test
		np ≥ 5	chi-square test
	Paired		McNemar or Kappa
3 or more groups	Independent	np < 5	collapse categories for chi-square test
		np ≥ 5	chi-square test
	Dependent		Cochran´s Q

[edit] Sources

• Dawson-Saunders, Beth; Robert G. Gapp (1994). Basic & Clinical biostatistics. Lange medical book. ISBN 0-8385-0542-2. 

[edit] See also

• Statistical hypothesis testing
• Parametric statistics
• Non-parametric statistics

[edit] External links

• NIST statistics
• Statsoft basic statistics