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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.
Fixed vs random effects in mixed models: what is the difference?
A fixed effect is a parameter you estimate for its own sake; a random effect treats groups as a sample from a wider population. An example in R and Python.
Read the answer →PCA, eigenvectors and eigenvalues explained in plain language
PCA finds the directions your data varies most. Eigenvectors are those directions; eigenvalues say how much variance each one carries.
Read the answer →What goes wrong if you sort x and y separately before a regression?
Sorting each variable on its own breaks the pairs, so the fit looks excellent whatever the real relationship is. It predicts new data worse than the mean.
Read the answer →Need more than an answer?
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