Quick answer: In jamovi: T-Tests → Independent Samples T-Test, move your numeric outcome into Dependent Variable and your two-level group into Grouping Variable — done. The real skill is the check-list around it: pick the right t-test variant, verify assumptions (normality, equal variances), and read the effect size, not just p. This walkthrough uses a worked example; the underlying logic is in our hypothesis testing guide.
Table of Contents
Which t-test do I need?
| Design | Menu choice |
|---|---|
| Two unrelated groups (drug vs placebo) | T-Tests → Independent Samples T-Test |
| Same people twice (pre-test, post-test) | T-Tests → Paired Samples T-Test |
| One group vs a known value (IQ vs 100) | T-Tests → One Sample T-Test |
Worked example: two teaching methods
40 students, exam scores, method A vs method B. Setup: Scores → Dependent Variable, Method → Grouping Variable. Tick Mean difference and Effect size (Cohen’s d) under Additional Statistics, and Normality + Homogeneity test (Levene’s) under Assumptions. Suppose output: A = 72.1 (SD 6.8), B = 66.4 (SD 7.5), t(38) = 2.46, p = 0.019, d = 0.78.
Reading it in order: the difference is 5.7 points with a 95% CI that excludes zero; p = 0.019 < 0.05 rejects “no difference” (what that means exactly: our p-value explainer); d = 0.78 is a medium-to-large effect — the finding matters beyond the p-value. The APA sentence writes itself: “Method A students scored higher (M = 72.1, SD = 6.8) than Method B (M = 66.4, SD = 7.5), t(38) = 2.46, p = .019, d = 0.78.”
The two assumption checks that matter
- Normality (Shapiro-Wilk): p > .05 is what you want. T-tests tolerate moderate violation with n ≥ 30 per group; for clearly skewed data use the Non-parametric pair (Mann-Whitney U) from the same menu.
- Equal variances (Levene’s): if p < .05, untick Student’s and tick Welch under Variance — jamovi prints the corrected row; degrees of freedom shift to decimals, which is expected, not an error.
Full first-session context (data types, variable setup, exporting) is in our jamovi first-analysis guide, and the biological decision logic behind test choice in hypothesis testing in biology. For one-to-one practice with an instructor checking your assumptions, Ampersand Academy teaches statistics with jamovi one-to-one.
Official documentation
For the full reference behind this guide, read the official jamovi site. It is the source the details above are drawn from, and the first place to check when an option or attribute name changes.
Frequently asked questions
How do I run an independent t-test in jamovi?
Analyses ribbon, T-Tests, Independent Samples T-Test. Move the numeric outcome to Dependent Variable and the two-level grouping variable to Grouping Variable. Results appear immediately with t, df and p.
Should I use Student’s or Welch’s t-test?
Run Levene’s test under the Assumptions section. If it is significant (unequal variances), use Welch’s – in jamovi just tick the Welch option. With similar variances and similar group sizes, Student’s is fine.
How do I interpret Cohen’s d in the jamovi output?
d is the group difference in standard-deviation units: 0.2 is small, 0.5 medium, 0.8 large. It communicates how big the effect is, which the p-value alone never tells you.
What if my data are not normal?
With roughly 30 or more per group, t-tests are robust to moderate non-normality. For small, clearly skewed samples, use the Mann-Whitney U (independent) or Wilcoxon rank (paired) tests from the same T-Tests menu.
Why does my grouping variable show only one group in the t-test?
The grouping variable must be a factor with exactly two levels. Check its measure is Nominal and that both levels actually appear in the data – filter rows or typo-coded levels are the usual culprit.
Last updated on · Written by Dinesh Kumar R
