Free tool

Which statistical test fits your study?

Four structured questions — outcome type, comparison structure, pairing, adjustment — mapped through a deterministic decision table to the recommended primary analysis. The answer leads with the estimand (what you will actually estimate and report), then the test, the assumptions to check, and the fallback if they fail.

§01 The picker

Describe the comparisonruns in your browser — nothing is uploaded
1 · What kind of primary outcome?
2 · What are you comparing?
3 · Adjusting for covariates or confounders?
Anything that changes the answer?
Pick an outcome type above to see the recommended approach.

What kind of study is this? (optional)

§02 The stances built in

This is not a neutral lookup table — it encodes methodological positions that reviewers and statisticians hold, so the recommendation is defensible, not just common:

  • Welch’s t by default for two independent means — not Student’s t behind a variance pre-test, which inflates type-I error.
  • No normality test to pick the test. At moderate n, normality tests over-detect trivial deviations; judge with a Q–Q plot and the estimand, and treat rank tests as a change of estimand, not a free upgrade.
  • An odds ratio is not a risk ratio when the outcome is common — cohort or trial data wanting a risk ratio get log-binomial or modified-Poisson regression, not a relabeled OR.
  • Matched designs get matched analyses — McNemar’s, conditional logistic, paired t — because breaking the matching is a top reviewer catch.
  • Estimation over testing: every recommendation reports an effect size with a confidence interval; the p-value is secondary.

This is one of the same deterministic engines that power RigorMD’s paid reviews — exposed free here, the identical rule table, not a simplified preview of it.

§03 Where it stops

Some designs need more than a table: cluster-randomized trials, non-inferiority and equivalence designs, Bayesian and adaptive designs, and any analysis where confounding strategy is the real question. If you tick “clustered,” the tool tells you a naïve test is wrong and a mixed-effects model or GEE is needed — it does not pretend the simple answer still holds. Where a question is beyond the table, the honest output is “this needs a biostatistician,” not a guess.

If you want that check done by a person, tell us below →.

§04 Want this reviewed by a person?

The test is one line of an analysis plan. If you want a statistician to check the rest — hypotheses, the variables and confounders to collect, the sample-size arithmetic for your effect size, a draft IRB statistical-methods page — tell us, and we will let you know if we build it. For what reviewers check beyond test choice, see what a pre-submission statistical review covers →.

Want a statistician-grade read of your study design before you collect data? We're gauging demand for a reviewed design assessment. Leave your email and we'll tell you if we build it.

A planning scaffold. The recommendation is a deterministic mapping from the structure you describe — it cannot see your data or your protocol. Confirm the final analysis choice with a qualified biostatistician before you enroll patients or submit to an IRB.