From the Analysis menu choose Compare groups, and choose the dependent variable(s) you want to analyze, choose the Grouping variable, then hit OK.

In the Display options... dialog, the minimum and maximum of the y-axis, and (new in v2.4) the way groups are displayed can be set.

The following results will be calculated (see also the common elements of the results):

Raw data

Result For interval variables For ordinal variables For nominal variables
Sample size Number of observed cases Number of observed cases Number of observed cases
Graphs of the raw data Plot with individual data Plot with individual data (order of the values) Mosaic plot

Sample properties

Result For interval variables For ordinal variables For nominal variables
Descriptive data Means, Standard deviations, Maximums, Upper quartiles, Medians, Lower quartiles, Minimums Maximums, Upper quartiles, Medians, Lower quartiles, Minimums Variation ratio, Contingency table (case count), Contingency table (percentage)
Standardized effect size For two groups Cohen’s d and eta-squared   Cramér’s V
Graphs of the data Box plot with individual data Box plot with individual data (order of the values)  

Population properties

Result For interval variables For ordinal variables For nominal variables
Population parameters numerically Mean with 95% CI Median and its confidence interval Contingency table (confidence interval, multinomial proportions with Goodman method)
Graphs of the population parameters Graph with mean and 95% CI of the mean    
Standardized effect size * For two groups, Hedges’ g and its confidence interval
* If one-way ANOVA was run, then omega-squared effect size.		    
Hypothesis test for two groups and sensitivity power analyses When one of the groups includes a single case, if the control group is normal (measured with Shapiro-Wilk test) then modified t-test, otherwise (up to v2.4) Mann-Whitney test, (from v2.5) Brunner-Munzel test. If the Single case slope is set, then slope comparison.

For comparing two groups if the data are normal (measured with Shapiro-Wilk test) and homoscedastic (measured with Levene’s test), then two-sample t-test and (new in v2.3) Bayesian two-samples t-test
For non-normal groups, (up to v2.4) Mann-Whitney test, (from v2.5) Brunner-Munzel test
For normal but heteroscedastic groups, Welch’s t-test
Confidence interval of the difference of the groups

For two-sample t-test sensitivity power analyses		 (up to v2.4) Mann-Whitney test, (from v2.5) Brunner-Munzel test Chi-squared test and sensitivity power analyses
Hypothesis test for more than two groups and sensitivity power analyses If the data are normal (measured with Shapiro-Wilk test) and homoscedastic (measured with Levene’s test) one-way ANOVA
If ANOVA is significant, post hoc Tukey’s HSD tests

Otherwise, Kruskal-Wallis test
If the Kruskal-Wallis test is significant, post hoc Dunn’s test

For one-way ANOVA sensitivity power analyses displayed as eta-square and as f.		 Kruskal-Wallis test

If the Kruskal-Wallis test is significant, post hoc Dunn’s test		 Chi-squared test and sensitivity power analyses
Hypothesis test for two or more grouping variables Two-way ANOVA or multi-way ANOVA