Overview of Statistical Tests

The table below shows which statistical methods can be used given the research set-up. The research set-up is based on 1) the type of independent variable(s) and 2) the type of dependent variable(s), which are listed in the rows and columns of the table respectively. Please click on a test to retrieve more information.

The most straightforward methods are shown per research set-up. However, in some cases, other methods can be used as well, and will often give the same or similar results. In order to keep the table relatively simple, these methods are omitted from some of the cells. If you want to add these methods to the cells, press the button below. These methods will have a dotted border in the table.

Dependent variable(s) or "Variable 2"
Independent variable(s) or "Variable 1" One categorical with 2 independent groupsOne categorical with $J$ independent groups ($J \geqslant 2$)One of ordinal levelOne quantitative of interval or ratio levelTwo or more quantitative of interval or ratio level
None
Goodness of fit test
$z$ test for a single proportion
Binomial test for a single proportion
Goodness of fit test
One sample Wilcoxon signed-rank test
One sample $z$ test for the mean
One sample $t$ test for the mean
2 paired groups
McNemar's test
Marginal Homogeneity test / Stuart-Maxwell test
Sign test
Paired sample $t$ test
Wilcoxon signed-rank test
One sample $t$ test for the mean
One categorical with 2 independent groups
Chi-squared test for the relationship between two categorical variables
$z$ test for the difference between two proportions
Logistic regression
Chi-squared test for the relationship between two categorical variables
Mann-Whitney-Wilcoxon test
Two sample $z$ test
Two sample $t$ test - equal variances not assumed
Two sample $t$ test - equal variances assumed
One way ANOVA
Regression (OLS)
One categorical with $I$ independent groups ($I \geqslant 2$)
Chi-squared test for the relationship between two categorical variables
Logistic regression
Chi-squared test for the relationship between two categorical variables
Kruskal-Wallis test
One way ANOVA
Regression (OLS)
Two categorical, the first with $I$ independent groups and the second with $J$ independent groups ($I \geqslant 2$, $J \geqslant 2$)
Logistic regression
Two way ANOVA
Regression (OLS)
Three or more categorical with independent groups
Logistic regression
Regression (OLS)
One or more categorical with independent groups, and one or more quantitative control variables of interval or ratio level (covariates)
Logistic regression
Regression (OLS)
One quantitative of interval or ratio level
Logistic regression
Pearson correlation
Regression (OLS)
One or more quantitative of interval or ratio level and/or one or more categorical with independent groups, transformed into code variables
Logistic regression
Regression (OLS)
One or more quantitative of interval or ratio level and/or one or more categorical with independent groups, transformed into code variables, plus at least one random factor
One of ordinal level
Spearman's rho
One within subject factor ($\geq 2$ related groups)
Cochran's Q test
Friedman test
One or more within subjects factors (related groups) and possibly one or more between subjects factors (independent groups)
One or more within subjects factors (related groups), one or more quantitative control variables of interval or ratio level (covariates), and possibly one or more between subjects factors (independent groups)