How to run a t-test in SPSS

To run a t-test in SPSS, you open Analyze > Compare Means, choose the version that matches your design, move your variables into the dialog, and read the significance value against your alpha of .05. The harder and more important part is choosing the right one of the three t-tests, checking the assumptions SPSS quietly relies on, and reporting the result with an effect size rather than a bare p-value. This guide walks the full procedure, the output tables, and the decisions a basic tutorial skips.

Which t-test your design needs first

SPSS offers three procedures under Compare Means, and the design dictates the choice. An independent-samples t-test compares the means of two separate groups, such as a treatment and a control condition. A paired-samples t-test compares two measurements taken from the same people, such as a pre-test and a post-test. A one-sample t-test compares one group mean against a known or hypothesised value. Counting your groups and noting whether the observations are independent or repeated settles the choice before you touch the menu, which is the same logic set out in matching the test to your design.

Checking the assumptions before you trust the result

A t-test assumes the dependent variable is roughly normally distributed within each group, that the two groups have similar variances (homogeneity of variance), and that observations are independent. Test normality in Analyze > Descriptive Statistics > Explore, reading the Shapiro-Wilk statistic for samples under about fifty and the histogram and Q-Q plot alongside it, because a significant Shapiro-Wilk on a large sample can flag trivial departures. You do not run a separate variance test: SPSS prints Levene's Test inside the t-test output itself. When normality is badly violated and the sample is small, switch to the rank-based nonparametric alternative, the Mann-Whitney U for independent groups or the Wilcoxon signed-rank test for paired data.

Running an independent-samples t-test step by step

Go to Analyze > Compare Means > Independent-Samples T Test. Move your continuous outcome into the Test Variable(s) box and your two-category grouping variable into Grouping Variable, then click Define Groups and enter the two codes (for example, 1 and 2). The equivalent syntax, which you should paste into a Syntax window and keep for reproducibility, is:

T-TEST GROUPS=group(1 2)
  /MISSING=ANALYSIS
  /VARIABLES=score
  /ES DISPLAY(TRUE)
  /CRITERIA=CI(.95).

The /ES DISPLAY(TRUE) subcommand, available from SPSS 27 onward, prints Cohen's d directly, so you no longer have to compute the effect size by hand.

Reading the independent-samples output correctly

SPSS returns two tables. Group Statistics gives the mean, standard deviation, and sample size per group; report these as your descriptive statistics before any inference. The Independent Samples Test table is where most students go wrong, because it prints two rows. Read Levene's Test for Equality of Variances first: if its significance is above .05, variances are similar and you read the Equal variances assumed row; if it is .05 or below, variances differ and you read the Equal variances not assumed row, which applies the Welch correction with adjusted degrees of freedom. From the correct row, take the t value, the degrees of freedom, the two-tailed significance, the mean difference, and its 95 percent confidence interval. Reading these tables is a skill in its own right, covered more widely in interpreting SPSS output.

Running paired-samples and one-sample t-tests

For a paired-samples t-test, choose Analyze > Compare Means > Paired-Samples T Test and move your two related variables into a single pair. SPSS tests whether the mean difference departs from zero, and the normality assumption applies to that difference score, not to the two raw variables. The syntax is:

T-TEST PAIRS=pretest WITH posttest (PAIRED)
  /ES DISPLAY(TRUE)
  /CRITERIA=CI(.95).

For a one-sample t-test, choose the matching menu item and type the comparison value into Test Value. Each procedure returns the same family of statistics, so the reporting logic below carries across all three.

Calculating and interpreting the effect size

A significant t-test tells you a difference is unlikely to be chance, but not whether it matters; that is the job of Cohen's d, the standardised mean difference. By convention, around 0.2 is a small effect, 0.5 is medium, and 0.8 is large, though you should interpret the value against your own field. For small samples, the Hedges correction that SPSS also reports removes a slight upward bias and is the safer figure to quote. Pairing the effect size with the significance test is what separates a defensible result from a thin one, and the reasoning behind it sits in our effect size explainer.

Reporting a t-test in APA style

Report the descriptive statistics, then the test in the standard format: the parenthetical carries the degrees of freedom, then the t value, the exact p-value, and the effect size. A clean independent-samples sentence reads: an independent-samples t-test found that post-intervention scores were higher in the treatment group (M = 24.6, SD = 4.1) than the control group (M = 21.2, SD = 3.8), t(98) = 4.27, p < .001, d = 0.85. When you read the Welch row, report the fractional degrees of freedom exactly as SPSS prints them. Full conventions, including italics and decimal rules, are in our APA statistics reporting guide, and this whole step feeds the results chapter.

Common t-test mistakes in a dissertation

The frequent errors are predictable and avoidable. Students read the wrong Levene's row, replace a single ANOVA with a stack of t-tests across three groups (which inflates the error rate ANOVA is meant to hold), apply an independent-samples test to repeated measurements, or report significance with no effect size. Test the difference score for normality in the paired case rather than the raw variables, decide your tails before you run the test, and never present a p-value alone. Handled with care, the t-test is the most transparent comparison in your analysis chapter.