The independent t-test, also called the two sample t-test or student's t-test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups.
The null hypothesis for the independent t-test is that the population means from the two unrelated groups are equal:
H0: u1 = u2
In most cases, we are looking to see if we can show that we can reject the null hypothesis and accept the alternative hypothesis, which is that the population means are not equal:
HA: u1 ≠ u2
To do this, we need to set a significance level (alpha) that allows us to either reject or accept the alternative hypothesis. Most commonly, this value is set at 0.05.
In order to run an independent t-test, you need the following:
Unrelated groups, also called unpaired groups or independent groups, are groups in which the cases in each group are different. Often we are investigating differences in individuals, which means that when comparing two groups, an individual in one group cannot also be a member of the other group and vice versa. An example would be gender - an individual would have to be classified as either male or female - not both.
The independent t-test requires that the dependent variable is approximately normally distributed within each group. We can test for this using a multitude of tests, but the Shapiro-Wilks Test or a graphical method, such as a Q-Q Plot, are very common. You can run these tests using SPSS, the procedure for which can be found in our Testing for Normality guide. However, the t-test is described as a robust test with respect to the assumption of normality. This means that even deviations away from normality do not have a large influence on Type I error rates. The exception to this is if the difference in the size of the groups is greater than 1.5 (largest compared to smallest).
If you find that either one or both of your group's data is not approximately normally distributed and groups sizes differ greatly, you have two options: (1) transform your data so that the data becomes normally distributed (to do this in SPSS see our guide on Transforming Data), or (2) run the Mann-Whitney U Test which is a non-parametric test that does not require the assumption of normality (to run this test in SPSS see our guide on the Mann-Whitney U Test).
The independent t-test assumes the variances of the two groups you are measuring to be equal. If your variances are unequal, this can affect the Type I error rate. The assumption of homogeneity of variance can be tested using Levene's Test of Equality of Variances, which is produced in SPSS when running the independent t-test. If you have run Levene's Test of Equality of Variances, whether in SPSS or by another means, you will get a result similar to that below:
This test for homogeneity of variance provides an F statistic and a significance value (p-value). We are primarily concerned with the significance level - if it is greater than 0.05, our group variances can be treated as equal. However, if p < 0.05, we have unequal variances and we have violated the assumption of homogeneity of variance.
If the Levene's Test for Equality of Variances is statistically significant, and therefore indicates unequal variances, we can correct for this violation by not using the pooled estimate for the error term for the t-statistic, and also making adjustments to the degrees of freedom using the Welch-Satterthwaite method. In all reality, you will probably never have heard of these adjustments as SPSS hides this information and simply labels the two options as "Equal variances assumed" and "Equal variances not assumed" without explicitly stating the underlying tests used. However, you can see the evidence of these tests as below:
From the result of Levene's Test for Equality of Variances, we can reject the null hypothesis that there is no difference in the variances between the groups and accept the alternative hypothesis that there is a significant difference in the variances between groups. The effect of not being able to assume equal variances is evident in the final column of the above figure where we see a reduction in the value of the t-statistic and a large reduction in the degrees of freedom (df). This has the effect of increasing the p-value above the critical significance level of 0.05. In this case, we therefore do not accept the alternative hypothesis and accept that there are no statistically significant differences between means. This would not have been our conclusion had we not tested for homogeneity of variances.
When reporting the result of an independent t-test, you need to include the t-statistic value, the degrees of freedom (df) and the significance value of the test (p-value). The format of the test result is: t(df) = t-statistic, p = significance value. Therefore, for the example above, you could report the result as t(7.001) = 2.233, p = 0.061.
In order to provide enough information for readers to fully understand the results when you have run an independent t-test, you should include the result of normality tests, Levene's Equality of Variances test, the two group means and standard deviations, the actual t-test result and the direction of the difference (if any). In addition, you might also wish to include the difference between the groups along with the 95% confidence intervals. For example:
Inspection of Q-Q Plots revealed that cholesterol concentration was normally distributed for both groups and that there was homogeneity of variance as assessed by Levene's Test for Equality of Variances. Therefore, an independent t-test was run on the data as well as 95% confidence intervals (CI) for the mean difference. It was found that after the two interventions, cholesterol concentrations in the dietary group (6.15 ± 0.52 mmol/L) were significantly higher than the exercise group (5.80 ± 0.38 mmol/L) (t(38) = 2.470, p = 0.018) with a difference of 0.35 (95% CI, 0.06 to 0.64) mmol/L.
To know how to run an independent t-test in SPSS, go to our SPSS guide here.