You run a t-test in SPSS under Analyze > Compare Means and Proportions, then pick the version that fits your design: an independent-samples t-test for two unrelated groups, a paired-samples t-test for two measurements on the same people, or a one-sample t-test to compare one mean against a fixed value. This page gives the exact menu path for each, shows the one value to read, and hands you copy-paste APA reporting.
A t-test compares means, but there are three of them in SPSS and each one fits a different design. Choose the wrong one, skip the assumption checks, or read the wrong row of the output, and the result is wrong no matter how tidy the table looks. This guide is part of our SPSS homework help and our wider statistics homework help. It gives the menu path for each t-test, points at the single value that decides the result, and shows how to report it in APA. Where a test can fail, it names the assumption and the fallback test to use instead.
Two version notes that trip people up. In SPSS version 29 and later the Compare Means menu is named Compare Means and Proportions. In version 27 and later the old Sig. (2-tailed) column is split into One-Sided p and Two-Sided p; for an ordinary non-directional hypothesis you read Two-Sided p.
Match your research question and design to the right t-test, then jump to its guide.
| Your question | Your data | Test |
|---|---|---|
| Do two unrelated groups differ on average? | Continuous outcome, two independent groups | Independent-samples t-test |
| Did the same people change between two points? | Continuous outcome, two paired measures | Paired-samples t-test |
| Does one group mean differ from a known value? | Continuous outcome, one group, one test value | One-sample t-test |
| Do three or more groups differ? | Continuous outcome, three or more groups | Use ANOVA instead |
Not sure your data meets the requirements? Every guide below lists the assumptions and the test to switch to when one fails.
For an independent-samples t-test your data needs two columns: one continuous Test Variable (the outcome) and one Grouping Variable that codes the two groups, for example one and two. For a paired-samples t-test the two measurements sit in two separate columns on the same row. For a one-sample t-test you need a single continuous column and a number to test it against.
The setting that quietly breaks the analysis is Measure in Variable View. SPSS tags every numeric variable as Scale by default. Leave the continuous outcome as Scale, but set the grouping variable to Nominal so SPSS treats the codes as categories rather than numbers. The t-tests all live under one menu: Analyze > Compare Means and Proportions.
Menu path, the assumption checks, the exact number to read, and how to report it. Example values follow standard teaching datasets.
Compares the means of one continuous outcome across two unrelated groups, for example test scores under an online course versus an in-person course.
Assumptions. Continuous outcome, two independent groups, no serious outliers, approximate normality within each group (Shapiro-Wilk in Analyze > Descriptive Statistics > Explore), and equal variances, which SPSS checks with Levene's Test inside the results table. If normality is badly broken, switch to the Mann-Whitney U test.
What to read, and the Levene's rule. In the Independent Samples Test table, let Levene's decide the row. If Levene's Sig. is above .05, read the top row, Equal variances assumed. If Levene's Sig. is .05 or below, read the bottom row, Equal variances not assumed. Then read t, df, and Two-Sided p on that row.
| Levene F | Levene Sig. | t | df | Two-Sided p | |
|---|---|---|---|---|---|
| Equal variances assumed | 1.82 | .184 | 2.310 | 48 | .025 |
| Equal variances not assumed | 2.310 | 46.3 | .026 |
Common mistakes
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Compares two measurements taken on the same people, for example a test-anxiety score before and after an intervention.
Assumptions. A continuous outcome measured twice on the same cases, no serious outliers, and normality of the difference scores, not the raw variables. Compute the difference with Transform > Compute Variable, then check it with a boxplot and Shapiro-Wilk. If the differences are badly non-normal, use the Wilcoxon signed-rank test.
What to read. The Paired Samples Test table, reading across to t, df, which equals the number of pairs minus one, and Two-Sided p. Do not read the Sig. in the Paired Samples Correlations table above it.
| Mean | Std. Deviation | t | df | Two-Sided p | |
|---|---|---|---|---|---|
| Anxiety Before - Anxiety After | 4.70 | 5.96 | 3.943 | 24 | <.001 |
Common mistakes
Compares one group mean against a known or hypothesised value, for example a class mean IQ against the population value of 100.
Assumptions. One continuous variable, independent observations, no serious outliers, and approximate normality (Shapiro-Wilk and a Q-Q plot in Explore). The one-sample t-test has no non-parametric partner in the Compare Means menu; if normality is badly broken, use the one-sample Wilcoxon signed-rank test under Analyze > Nonparametric Tests instead.
What to read. The One-Sample Test table, reading t, df, which equals the sample size minus one, Two-Sided p, and the Mean Difference, which is your sample mean minus the Test Value. The Test Value itself is printed in the table heading so you can confirm you typed the right one.
| t | df | Two-Sided p | Mean Difference | |
|---|---|---|---|---|
| IQ score | 2.857 | 39 | .007 | 5.60 |
Common mistakes
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Levene's Test appears only in the independent-samples t-test, and it decides which of the two rows you read. It tests whether the two groups have equal variances (spread). Read its Sig. first. If Levene's Sig. is above .05 the variances are close enough, so you read the top row, Equal variances assumed. If Levene's Sig. is .05 or below the variances differ, so you read the bottom row, Equal variances not assumed, where SPSS has already applied the Welch correction and the df often become fractional. The paired and one-sample tests have no Levene's row because there is only one set of scores.
Two-Sided p is the p-value for an ordinary two-tailed hypothesis, the one you almost always want. In SPSS version 27 and later the single Sig. (2-tailed) column became two columns, One-Sided p and Two-Sided p. Use One-Sided p only when your hypothesis states a direction in advance. If the value shows as .000, report it as p < .001, because a probability is never exactly zero. For more on reading every table, see our guide to interpreting SPSS output.
Most t-test marks are lost not on the software but on ignoring a broken assumption. Check the assumption, and when it fails, switch to the matched test rather than reporting an invalid result.
| Assumption | How SPSS checks it | If it fails |
|---|---|---|
| Normality (independent groups) | Shapiro-Wilk and Q-Q plots in Explore | Use the Mann-Whitney U test |
| Normality (paired differences) | Shapiro-Wilk on the computed difference score | Use the Wilcoxon signed-rank test |
| Normality (one sample) | Shapiro-Wilk and Q-Q plots in Explore | Use the one-sample Wilcoxon signed-rank test |
| Equal variances (independent only) | Levene's Test in the results table | Read the "equal variances not assumed" row |
| Three or more groups | The design, not a test | Use one-way ANOVA |
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Use an independent-samples t-test for two unrelated groups, a paired-samples t-test for two measurements on the same people, and a one-sample t-test to compare one group mean against a known or hypothesised value. For three or more groups use ANOVA.
Levene's Test checks whether the two groups have equal variances. If its Sig. is above 0.05, read the top row, Equal variances assumed. If its Sig. is 0.05 or below, read the bottom row, Equal variances not assumed. Then read t, df and Two-Sided p on that same row.
Two-Sided p is the p-value for an ordinary non-directional hypothesis. In SPSS version 27 and later the old Sig. (2-tailed) column is split into One-Sided p and Two-Sided p; for a standard two-tailed t-test you read Two-Sided p. If it is below 0.05 the difference is statistically significant.
Switch to the matched non-parametric test. Use the Mann-Whitney U test in place of the independent-samples t-test, and the Wilcoxon signed-rank test in place of the paired-samples t-test. Check normality with Shapiro-Wilk and Q-Q plots, and for the paired test check the difference scores, not the raw variables.
SPSS rounds very small p-values to .000, but a probability is never exactly zero. Report it as p < .001.
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