Answer a few simple questions about your research and data, and this tool recommends a statistical test, with a direct link to the calculator. Each question also has a short Roman Urdu explanation.
How the test chooser decides
- Your goal: comparing groups, finding a relationship, checking reliability, planning sample size or describing data.
- Type of outcome: continuous scores versus categories (counts).
- Number of groups and whether they are independent or paired.
- Normality: parametric tests for roughly normal data, non-parametric alternatives otherwise.
Frequently asked questions
How do I choose the right statistical test?
Start from your research question (compare groups, find a relationship, check reliability), then consider the type of outcome variable, the number of groups, whether the groups are independent or paired, and whether the data are roughly normal.
What is the difference between parametric and non-parametric tests?
Parametric tests (t tests, ANOVA, Pearson) assume roughly normal continuous data. Non-parametric tests (Mann-Whitney, Kruskal-Wallis, Spearman) use ranks and are safer for small, skewed or ordinal data.
Which test should I use for Likert scale data?
For totals or means of multi-item Likert scales, parametric tests such as t tests, ANOVA and Pearson are widely used. For single Likert items, non-parametric tests are safer.
Which test compares more than two groups?
One-way ANOVA (followed by Tukey HSD) for normal data, or the Kruskal-Wallis test for non-normal or ordinal data.