What Is the Independent Samples T-Test?
The independent samples t-test compares the means of two unrelated groups on a continuous variable to determine whether the difference is statistically significant. Classic applications: Do male and female students differ on exam scores? Is there a significant difference in outcomes between a treatment and control group?
Assumptions
- Normality: The dependent variable should be approximately normally distributed within each group. Check with Shapiro-Wilk (p>0.05 supports normality).
- Homogeneity of variance: Tested by Levene's test. If p>0.05, equal variances are assumed; if p<0.05, use the "Equal variances not assumed" row.
- Independence: Each participant belongs to one group only.
Running the Test in SPSS
Navigate to Analyze → Compare Means → Independent-Samples T Test.
- Move the dependent variable (scores, measurements) to Test Variable(s).
- Move the grouping variable (gender, condition) to Grouping Variable.
- Click Define Groups and enter the two group codes (e.g., 1 and 2).
- Click OK.
Reading the Output
SPSS produces two tables. Group Statistics shows means and standard deviations per group. Independent Samples Test shows Levene's test and the t-test results. Use the correct row based on Levene's result, then look at Sig. (2-tailed) for your p-value.
- p<0.05 → statistically significant difference between groups
- Calculate Cohen's d for effect size: d = (M₁−M₂) / SDpooled. Small: 0.20, Medium: 0.50, Large: 0.80.
APA 7 Reporting Example
An independent samples t-test indicated that the experimental group (M=78.4, SD=9.2) scored significantly higher than the control group (M=71.6, SD=10.1), t(98)=3.42, p=.001, d=0.69.
When Normality Is Violated
If the normality assumption is not met — especially in small samples — use the Mann-Whitney U test, the non-parametric alternative. Boss Statistics can guide you through assumption testing and test selection.