Type of question

Type of question: Do data match an expected ratio?

Is there an association between two variables?

Type of data: discrete, categorical (counts,frequencies)

-goodness of fit test with Yates’s corr.; Fischer’s exact !

"

!

2

non-parametric (nominal, ordinal, interval)

goodness of fit test

"

-test for heterogeneity

"

simple linear regression (**)

association with Yates’s corr.; Fischer’s exact

association

two variables, data can be ranked (ordinal, interval)

# categories two two or more

data: unpaired paired

Pearson’s product moment correlation

two variables, both variables at least one variable have two categories has more than two categories

" 2 -test for

# treatment variables: one two

2

!

" 2 -test for

Type of data: non-parametric

parametric (interval)

two variables expl. var. under neither one under control or with control and with smaller error similar error than resp. var.

one variable, two or more samples, two or more categories

2

. Type of data:

a priori expectation? yes no

one variable, one sample # categories: two more than two

Do samples come from the same or different populations?

Spearman’s rank correlation

one-way anova, Kruskal-Wallis

# observ. per sample less than 30 at least 30 principal axis regression (**)

# observ. per sample less than at least 30 30 (and data continuous)

Wilcoxon’s rank paired test (***)

Mann-Whitney U test (sometimes called Wilcoxon test) (***)

z-test for unpaired data (*)



Tom de Jong Frans Jacobs

# treatment variables: one two or more # categories two two or more

one-way parametric anova (*), (**)

variances homogeneous yes no

# observ. per sample less than at least 30 30

t-test for unpaired data (*)

multi-way parametric anova (possibly nested) (*), (**)

data: unpaired paired

Welch t-test

z-test for paired data (*)

! Always check the literature for the details! (*): requires homogeneous variances (F test) (**): check for normally distributed residuals (***): requires similarly shaped distributions

two-way nonparametric anova

parametric

# observ. per sample less than at least 30 30

t-test for paired data (*)

z-test for unpaired data (*)

If you have data for which no test seems available, try to transform your data.

z-test for paired data (*)