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Interactive guide
Which statistical test should I use?
Answer a few plain-language questions. You'll get a suggested test, what to check before you trust it, and links to free tools.
A starting point, not a substitute for looking at your data. Design details such as clustering, missing data, and multiple comparisons can change the right answer.
Free resources
Free tools and guides
These live on the DASS site and are free to use.
Tools
Power analysis calculators
Plan sample size for t-tests, ANOVA, correlations, chi-square, and multilevel designs.
Open calculators →APA 7 results formatters
Turn statistical output into clean, copy-ready APA sentences. Runs in your browser.
Open formatters →Check a reported p-value
Recompute the exact p from a t, F, chi-square, or correlation statistic and compare it to what was reported.
Check a p-value →Guides
Answers
Recent questions, answered
Plain-language answers to statistics questions people are asking.
Mann-Whitney effect size: is z/√N right, and which CI should you report?
Why z/√N is a shaky effect size for Mann-Whitney, why Fisher's z interval does not suit it, and which effect sizes and intervals to report instead.
Read the answer →Should you check normality of the outcome variable or of the residuals?
Linear models assume normal errors, not a normal outcome. A simulation shows how checking Y instead of the residuals misleads in both directions.
Read the answer →Training, validation and test sets: what is each one for?
What training, validation and test data are each for, why a validation score is too optimistic, and when cross-validation replaces it.
Read the answer →Need more than an answer?
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