Independent-Samples T Test
The Independent-Samples T Test procedure compares means for two groups of cases. Ideally, for this test, the subjects should be randomly assigned to two groups, so that any difference in response is due to the treatment (or lack of treatment) and not to other factors. This is not the case if you compare average income for males and females. A person is not randomly assigned to be a male or female. In such situations, you should ensure that differences in other factors are not masking or enhancing a significant difference in means. Differences in average income may be influenced by factors such as education (and not by sex alone).
Example. Patients with high blood pressure are randomly assigned to a placebo group and a treatment group. The placebo subjects receive an inactive pill, and the treatment subjects receive a new drug that is expected to lower blood pressure. After the subjects are treated for two months, the two-sample t test is used to compare the average blood pressures for the placebo group and the treatment group. Each patient is measured once and belongs to one group.
Statistics. For each variable: sample size, mean, standard deviation, and standard error of the mean. For the difference in means: mean, standard error, and confidence interval (you can specify the confidence level). Tests: Levene's test for equality of variances and both pooled-variances and separate-variances t tests for equality of means.
Independent-Samples T Test Data Considerations
Data. The values of the quantitative variable of interest are in a single column in the data file. The procedure uses a grouping variable with two values to separate the cases into two groups. The grouping variable can be numeric (values such as 1 and 2 or 6.25 and 12.5) or short string (such as yes and no). As an alternative, you can use a quantitative variable, such as age, to split the cases into two groups by specifying a cutpoint (cutpoint 21 splits age into an under-21 group and a 21-and-over group).
Assumptions. For the equal-variance t test, the observations should be independent, random samples from normal distributions with the same population variance. For the unequal-variance t test, the observations should be independent, random samples from normal distributions. The two-sample t test is fairly robust to departures from normality. When checking distributions graphically, look to see that they are symmetric and have no outliers.
To Obtain an Independent-Samples T Test
This feature requires the Statistics Base option.
- From the menus choose:
- Select one or more quantitative test variables. A separate t test is computed for each variable.
- Select a single grouping variable, and then click Define Groups to specify two codes for the groups that you want to compare.
- Optionally, click Options to control the treatment of missing data and the level of the confidence interval.
This procedure pastes T-TEST command syntax.