Which Test Should I Use?
Answer a few plain-English questions about your data and design to get a recommended test and a link to the interactive lesson that teaches it. You don't need to know any jargon.
A recommendation, not a verdict. Real analyses depend on details a flowchart cannot capture. Check the test's assumptions (ANOVA & t-Test Assumptions), and remember that a significant p-value still needs an effect size next to it.
Pick one test, not many. Running several of these on the same data and keeping whichever turns up p < .05 is a quick way to get a false positive, an example of the garden of forking paths. Decide your analysis before you look at the data, ideally by preregistering it.
Planning a whole study? The Plan My Analysis wizard takes the test you reach here and builds a full, printable plan: assumptions to check, the sample size you need, and an APA write-up skeleton. Plan your analysis →
The whole map on one screen
Prefer to scan? The table below is the cheat-sheet version, and every test opens its interactive lesson. Want it on paper? Print the one-page which-test cheat sheet. Want the full explanation in prose, with advice on messy designs? Read Choosing Statistics for Your Dissertation.
| Your situation | Test → lesson | Section |
|---|---|---|
| One group vs. a known value | One-sample t-test | 1.13 |
| Same people, measured twice | Paired t-test | 1.13 |
| Two separate groups | Independent t-test (Welch) | 1.14 |
| 3+ separate groups, one factor | One-way ANOVA | 2.2 |
| 3+ groups, two crossed factors | Factorial ANOVA | 2.4 |
| Same people, 3+ conditions | Repeated-measures ANOVA | 2.5 |
| Group comparison, adjusting for a covariate | ANCOVA | 2.15 |
| Groups compared on several outcomes at once | MANOVA | 3.4 |
| Any of the above, assumptions broken | Rank-based alternative | 2.1 |
| Two continuous variables, association | Pearson correlation | 1.17 |
| Two categorical variables, association | Chi-square test | 1.16 |
| Predict a number, one predictor | Simple linear regression | 1.18 |
| Predict a number, several predictors | Multiple regression | 2.9 |
| Predict yes/no | Logistic regression | 2.16 |
| Predict a count | Poisson / negative binomial GLM | 3.9 |
| Nested / clustered / repeated data | Mixed (multilevel) model | 3.10 |
| Time until an event, with dropouts | Kaplan–Meier / survival analysis | 3.16 |
| Combining many studies | Meta-analysis | 3.18 |
| Causal effect, assigned by something as good as random | Instrumental variables (2SLS) | 3.13 |
| Causal effect, assigned by a strict cutoff on a score | Regression discontinuity | 3.14 |
| Causal effect, a policy that arrived in some places first | Difference-in-differences | 3.15 |
| Predict as accurately as possible (not test a hypothesis) | Machine-learning models | ML 5.1 |