Paired-Samples t-Test: How to Run It Online and Report It in APA7
The paired-samples t-test compares two measurements from the same people (or matched pairs) by testing whether the average difference is zero.
What is Paired-Samples t-Test?
The paired-samples t-test compares two measurements from the same people (or matched pairs) by testing whether the average difference is zero.
When to use it
Use it for before and after designs, two conditions tested on the same participants, or matched pairs such as twins.
Example question: Did anxiety scores change from pre-test to post-test?
Preparing your data
Wide format: one row per participant and two numeric columns, for example pre and post.
Example dataset (simulated; first rows of 30): id, pre, post (anxiety scores for the same participants); wide format.
| id | pre | post |
|---|---|---|
| 1.0 | 25.2 | 19.7 |
| 2.0 | 31.0 | 27.8 |
| 3.0 | 11.6 | 5.7 |
| 4.0 | 20.2 | 12.8 |
| 5.0 | 30.0 | 30.2 |
| 6.0 | 38.4 | 37.0 |
Download the full example CSV to practise. If the tool asks for a different layout, follow the instructions on its page.
How to run it on StatFas
- Open the Paired-Samples t-Test tool. You do not need an account to run an analysis.
- Enter your data or upload your file, laid out as described in Preparing your data.
- Select your variables. Select the two paired columns.
- Choose any options the tool offers (for example the confidence level, a post hoc test or the error bar type) and run the analysis.
- Read the results on the page. Check the assumptions first, then the test statistic, the p-value and the effect size.
- To save your results, log in or create a free account. Every analysis stays free; the account is only needed to download.
Assumptions and checks
- The pairs are meaningful: the same participants, or properly matched pairs.
- The differences (post minus pre) are roughly normal, or the number of pairs is large.
- Pairs are independent of one another.
- No extreme outliers in the differences.
Key numbers to look at
- Means and SDs for both time points.
- Mean difference with 95% CI.
- t(df), p with df = number of pairs − 1.
- Effect size dz = mean difference ÷ SD of the differences.
Effect size
Report dz or dav and say which. About 0.2 small, 0.5 medium and 0.8 large.
Benchmarks such as small, medium and large are conventions (Cohen, 1988), not rules. Interpret size in the context of your field.
Reporting Paired-Samples t-Test in APA7 style
Template
Worked example (simulated data)
The numbers below were computed from the example CSV above, so you can reproduce them. They illustrate the format and are not real findings.
APA7 reminders: italicise statistical symbols (M, SD, t, F, p, r), report exact p-values to two or three decimals (use p < .001 for very small values), omit the leading zero for values that cannot exceed 1 (.45, not 0.45), and put degrees of freedom in parentheses. See the APA7 cheat sheet.
Common mistakes
- Running an independent test on paired data, which wastes power.
- Including participants with only one of the two measurements without saying so.
- Not stating which version of the effect size was used.
Related analyses and alternatives
- Wilcoxon Test: Non-normal differences or ordinal data.
- One-way ANOVA with repeated measures: Three or more time points.
- Independent t-Test: Different people in each condition.
Not sure which analysis you need? Use the test chooser.
Frequently asked questions
How do I get the degrees of freedom?
The number of pairs minus one.
Do I need equal variances?
No. The test uses the differences within pairs, so equal variance between the two conditions is not required.
References
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
This guide is for learning and for preparing results for your own reports. You remain responsible for choosing an appropriate analysis, checking its assumptions and interpreting the results. Follow the formatting rules of your journal or institution where they differ from APA7.