Statistical Method Advisor
"Which statistical method should I use for my data?" — answer a few questions and the advisor recommends a statistical method and takes you straight to the matching online tool. Covers common scenarios: group comparison, correlation/regression, diagnostic tests, survival analysis, and agreement/reliability. For method selection only; make the final call in light of your study design.
What is the goal of your study?
How to use & methodology
Can this advisor make the decision for me?
It gives a 'recommended direction' for common situations to help you quickly settle on a suitable method and go straight to the tool. The actual choice also depends on the study design, data distribution, sample size, and meaning of the variables; for complex or atypical designs, consult a statistician.
How do I judge whether data are normal?
Look at a histogram/Q-Q plot or run a normality test; with larger samples, t-tests/ANOVA are fairly robust to departures from normality, while for small samples or clear skew, prefer non-parametric methods. When unsure, take the 'skewed/unsure' branch — non-parametric is the safer choice.
What if the method I need isn't among the recommended tools?
This site keeps adding tools. If a method you need (e.g. Poisson regression, mixed-effects models) isn't live yet, use the closest method for now, or tell us via the feedback link.
Why are there both parametric and non-parametric recommendations for the same kind of question?
Parametric methods (t-test, ANOVA, Pearson) have higher power but require assumptions like normality/equal variance; non-parametric methods (Mann-Whitney, Kruskal-Wallis, Spearman) have looser assumptions and are more robust to skew and outliers. Choose based on whether your data meet the assumptions.