Paired samples t-test
Paired samples t-test
The paired t-test is used to test the null hypothesis that the average of the differences between a series of paired observations is zero. Observations are paired when, for example, they are performed on the same samples or subjects.
Select the variables for sample 1 and sample 2, and a possible filter for the data pairs. You can use the button to select variables and filters in the variables list.
Logarithmic transformation: if the data require a logarithmic transformation (e.g. when the data are positively skewed), select the Logarithmic transformation option.
The results windows for the paired samples t-test displays the summary statistics of the two samples. Note that the sample size will always be equal because only cases are included with data available for the two variables.
Next, the arithmetic mean of the differences (mean difference) between the paired observations is given, the standard deviation of these differences and the standard error of the mean difference followed by the 95% confidence interval for the mean difference.
Differences are calculated as sample 2 - sample 1.
In the paired samples t-test the null hypothesis is that the average of the differences between the paired observations in the two samples is zero.
If the calculated P-value is less than 0.05, the conclusion is that, statistically, the mean difference between the paired observations is significantly different from 0.
Note that in MedCalc P-values are always two-sided.
If you selected the Logarithmic transformation option, the program performs the calculations on the logarithms of the observations, but reports the back-transformed summary statistics.
For the paired samples t-test, the mean difference and 95% confidence are given on the log-transformed scale.
Next, the results of the t-test are transformed back and the interpretation is as follows: the back-transformed mean difference of the logs is the geometric mean of the ratio of paired values on the original scale (Altman, 1991).
- Altman DG (1991) Practical statistics for medical research. London: Chapman and Hall.
- To perform different tests in one single procedure, see Comparison of paired samples
- Independent samples t-test
- Student's t-test on Wikipedia.