ttest: compare means between two groups

Estimate a difference in group means, quantify its uncertainty, and separate statistical evidence from causal interpretation.

Tested 2026-08-06 with the current stats.camp development build · View the do-file on GitHub

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Complete example

The example compares math scores between the two values of female in a 24-row synthetic teaching dataset.

clear
import delimited "data/student_scores.csv"
ttest math, by(female)

With two independent groups, the default command estimates their means and tests the null hypothesis that the population mean difference is zero under an equal-variance model.

Output from stats.camp

Two-sample t test with equal variances
------------------------------------------------------------------------------
       Group |     Obs        Mean   Std. err.   Std. dev.     [95% conf. interval]
-------------+----------------------------------------------------------------
           0 |      12   52.916667    1.835914    6.359793     48.875847   56.957486
           1 |      12   61.833333    1.795758    6.220689     57.880896    65.78577
-------------+----------------------------------------------------------------
    Combined |      24      57.375    1.562482    7.654567      54.14276    60.60724
-------------+----------------------------------------------------------------
        diff |           -8.916667    2.568137                -14.242657   -3.590676
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                         t =  -3.4720
H0: diff = 0                         Degrees of freedom = 22

    Ha: diff < 0              Ha: diff != 0              Ha: diff > 0
 Pr(T < t) = 0.0011      Pr(|T| > |t|) = 0.0022      Pr(T > t) = 0.9989

How to read the result

Group 0 has a mean of about 52.92 and group 1 has a mean of about 61.83. Because the reported difference is mean(0) - mean(1), the estimate is −8.92 points. Its 95% confidence interval is approximately −14.24 to −3.59, and the two-sided p-value is 0.0022.

The data are invented, and the grouping variable was not randomly assigned. The result demonstrates command interpretation; it is not evidence about real students and does not identify a causal effect.

Assumptions, variants, and limits

Check that observations are independent and that a mean-based comparison is appropriate for the outcome and sample size.
The default two-sample form assumes equal population variances. stats.camp also supports unequal and welch alternatives for the corresponding model.
One-sample and paired tests answer different questions; choose the form from the study design rather than the observed p-value.
A small p-value is not an effect size, a measure of practical importance, or proof of causality.

See the current ttest entry and Stata's ttest manual for an external reference.

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