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SPSS Output Explorer
Explore example SPSS tables and learn what each value means. This free tool uses simulated data—it does not run statistical analyses or read your own results.
Select any highlighted value to see what it means, why it matters and how it is reported in APA style.
Independent-samples t-test
Do students who complete a six-week, self-guided online wellbeing programme have different wellbeing scores from students on a waitlist? 60 first-year volunteers were randomly allocated to one or the other.
Only the parts of the output needed to read this result are shown. Where SPSS prints more, a note under the table says what has been left out and why.
| Group | N | Mean | Std. Deviation | Std. Error Mean | |
|---|---|---|---|---|---|
| Wellbeing | Programme | 30 | .816 | ||
| Waitlist | 30 | .941 |
| Levene's Test for Equality of Variances | t-test for Equality of Means | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| F | Sig. | t | df | Significance | Mean Difference | Std. Error Difference | 95% Confidence Interval of the Difference | ||||
| One-Sided p | Two-Sided p | Lower | Upper | ||||||||
| Wellbeing | Equal variances assumed | 1.246 | |||||||||
| Equal variances not assumed | .010 | .019 | 3.000 | 1.246 | .506 | 5.494 | |||||
End of the Independent Samples Test table
| Standardizer | Point Estimate | ||
|---|---|---|---|
| Wellbeing | Cohen's d | 4.824 | |
The Standardizer is the denominator used to estimate the effect size. Cohen's d uses the pooled standard deviation.
Selected output. Not shown: the 95% confidence interval columns, and the Hedges' correction and Glass's delta rows. SPSS prints these, but its calculation methods for them have not yet been independently confirmed, so they are left out rather than risk showing an incorrect value.
About this example
60 first-year volunteers were randomly allocated to a six-week, self-guided online wellbeing programme (30 students) or a waitlist (30 students). Each student completed the programme on their own, so no group sessions link one student's score to another's. Wellbeing was measured once, at week 6, with a 10-item questionnaire (each item rated 1 to 4; total 10 to 40).
Limitations
- The data are simulated for teaching.
- Volunteers may not represent all students.
- Wellbeing is self-reported.
- Wellbeing was measured only after the programme. Random allocation balances the groups on average, not exactly, so a baseline difference cannot be ruled out.
- The total of a rating scale is treated as approximately continuous.
What can be concluded
Because students were randomly allocated, a statistically significant difference between the groups supports a causal interpretation: the programme may have contributed to the Programme group's higher scores at week 6. This interpretation holds only within the limitations above, including that the data are simulated for teaching. Wellbeing was measured only after the programme, with no baseline, so the results show that the Programme group scored higher than the Waitlist group at week 6. They do not show that wellbeing improved or changed over time. Report the result cautiously: say which group scored higher and whether the difference is statistically significant, and give the effect size and confidence interval.
Working on your own independent-samples t-test?
This Explorer is a free teaching resource. Its examples use simulated data, and it does not run analyses or look at your own output.
The Result Decoder (paid) is for your own results: you enter the values from your completed analysis and get an APA 7 results paragraph, a plain-English interpretation, checks on the values you entered, effect-size guidance, charts where available and Defend This Result practice.
Free guide: How to Report an Independent-Samples t-Test in APA 7