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Stuck on a statistics question?
Tell us what you're trying to figure out, in plain words. Someone on our team will read it and reply by email within two business days.
- No jargon required. Describe your data and your goal the way you'd explain it to a colleague.
- Theses, studies, program evaluations, surveys, business data.
- Rather work it out yourself? Try the test chooser below.
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
- Build a Raw-Score-to-T-Score Crosswalk for a Short Form
- Reviewer 2 Criticized Your Shortened Scale? How to Defend It
- How to Shorten a Survey Scale Without Losing Validity
- The Data Audit Checklist to Run First
- FIML vs. Multiple Imputation: Which to Use?
- When a Reviewer Challenges Your Model
- Power for Cluster-Randomized Trials Isn't Simple
- How Much Does a Statistical Consultant Cost?
Answers
Recent questions, answered
Plain-language answers to statistics questions people are asking.
Mean, median or quantile: which point prediction should a model give?
Why the loss you will be judged on, not the likelihood you fitted, decides whether a point prediction should be the conditional mean, the median or a quantile.
Read the answer →Bayesian vs frequentist statistics in plain English
Frequentists treat probability as long-run frequency; Bayesians treat it as degree of belief. Here is what that changes, with one example done both ways.
Read the answer →Why Euclidean distance breaks down in high dimensions
Why distances between points become nearly equal as features pile up, and how to tell if clustering on 100 features is still trustworthy.
Read the answer →Need more than an answer?
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