Prism offers seven related tests that compare two groups. To choose among these tests, answer three questions in the Experimental Design tab of the t test parameters dialog:
Choose a paired test when the columns of data are matched. That means that values on the same row are related to each other.
Here are some examples:
•You measure a variable in each subject before and after an intervention.
•You recruit subjects as pairs, matched for variables such as age, ethnic group, and disease severity. One of the pair gets one treatment; the other gets an alternative treatment.
•You run a laboratory experiment several times, each time with a control and treated preparation handled in parallel.
•You measure a variable in twins or child/parent pairs.
Matching should be determined by the experimental design, and definitely should not be based on the variable you are comparing. If you are comparing blood pressures in two groups, it is OK to match based on age or postal code, but it is not OK to match based on blood pressure.
Nonparametric tests, unlike t tests, are not based on the assumption that the data are sampled from a Gaussian distribution. But nonparametric tests have less power. Deciding when to use a nonparametric test is not straightforward.
After defining the experimental design, and the general approach (parametric or nonparametric), you need to decide exactly what test you want Prism to perform.
Decide whether to accept the assumption that the two samples come from populations with the same standard deviations (same variances). This is a standard assumption of the unpaired t test. If don't wish to make this assumption, Prism will perform the unequal variance (Welch) unpaired t test.
Choose the paired t test (which is standard in this situation) or the ratio t test (which is less standard). Choose the paired t test when you expect the differences between paired values to be a consistent measure of treatment effect. Choose the ratio paired t test when you expect the ratio of paired values to be a consistent measure of treatment effect.
Prism offers two choices: The MannWhitney test and the KolmogorovSmirnov test. It is hard to offer guidelines for choosing one test vs. the other except to follow the tradition of your lab or field. The main difference is that the MannWhitney test has more power to detect a difference in the median, but the KolmogorovSmirnov test has more power to detect differences in the shapes of the distributions.
MannWhitney test 
KolmogorovSmirnov test 

Power to detect a shift in the median 
More power 
Less power 
Power to detect differences in the shape of the distributions 
Less power 
More power 
In this case there is no choice. Prism will perform the Wilcoxon matched pairs test.