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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.
How do you interpret the ICC when random effects are nested?
With readings nested in stations nested in sites, one model gives several ICCs. Which one answers which question, with R and Python examples.
Read the answer →Significant ANOVA but no significant pairwise comparisons: how can that happen?
Why a one-way ANOVA F test can reject while every pairwise or Tukey comparison does not (and the reverse), and how far apart their p values can get.
Read the answer →t tests vs the F test in regression: what is the difference?
Why regression output gives a t test for each coefficient and one overall F test, when the two agree exactly, and why they can point in opposite directions.
Read the answer →Need more than an answer?
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