Statistical test selector
Answer a few questions about your design and get the test that fits, with the reasoning shown rather than hidden.
A statistical test selector turns three facts about your study, the type of outcome variable, what you are trying to do with it, and whether the assumptions hold, into the test that matches. The choice between a t-test, ANOVA, correlation, a regression, or a non-parametric alternative is not a matter of taste: it follows from the measurement level of your data and your research question. This tool walks that logic and links to the guide behind each recommendation.
Recommended test
Independent-samples t-test
Two independent groups on a continuous, roughly normal outcome are compared with the independent-samples t-test.
Read the reasoning
How the recommendation is reached
The selector follows the same decision tree a statistician uses. The first branch is the outcome variable: a continuous measurement points toward means and the parametric family, ordinal data toward rank-based tests, and unordered categories toward the chi-square family. The second branch is the goal, comparing groups, measuring an association, or predicting, and the third is whether the parametric assumptions hold.
Comparing two independent groups on a continuous outcome gives an independent-samples t-test when normality is reasonable, and the Mann-Whitney U test when it is not. Three or more groups move to ANOVA or the Kruskal-Wallis test. Paired measurements switch to the paired t-test, Wilcoxon signed-rank, or repeated-measures ANOVA. Association questions resolve to Pearson or Spearman correlation, or chi-square for two categorical variables, and prediction resolves to linear or logistic regression by outcome type. The recommendation is a starting point: it assumes you have checked the assumptions it names.
Frequently asked questions
- What statistical test should I use for categorical data?
For categorical (nominal) outcomes, the chi-square test of independence checks whether two categorical variables are associated, while a test of two proportions compares event rates between groups. When expected cell counts are small, Fisher's exact test replaces chi-square. Set the outcome to nominal in the selector and it points you to the right one.
- What statistical test should I use for ordinal data?
Ordinal outcomes, such as Likert ratings, are usually analysed with rank-based nonparametric tests: the Mann-Whitney U test for two groups and the Kruskal-Wallis test for three or more. Spearman's rank correlation measures the association between two ordinal variables. Setting the outcome to ordinal returns the match for your design.
- What test is used to compare two groups?
For a continuous outcome, an independent-samples t-test compares two separate groups and a paired t-test compares two measurements on the same participants, with the Mann-Whitney U test as the nonparametric alternative. The right choice depends on whether the groups are independent and whether the data meet the normality assumption, both of which the selector asks about.
- What statistical test shows the relationship between two variables?
To quantify the relationship between two continuous variables, use Pearson's correlation when the association is linear and both variables are roughly normal, or Spearman's correlation otherwise. When you want to predict one variable from another, linear regression is the tool. Choose association or prediction as your goal to get a recommendation.