Paired Continuous Data (Paired T-Test, Wilcoxon Signed Rank Test)

Abstract

Studies where two outcomes in one patient are compared with one another, are often called crossover studies, and the observations are called paired observations. As paired observations are usually more similar than unpaired observations, special tests are required in order to adjust for a positive correlation between the paired observations.

Significant effects indicate that the null-hypothesis of no difference between the two outcome can be rejected. The treatment 1 performs better than the treatment 2. It may be prudent to use the non-parametric tests, if normality (Gaussian-like frequency distribution) is doubtful like in most small data samples. Paired t-tests and Wilcoxon signed rank tests need, just like multivariate data, more than a single outcome variable. However, they cannot assess the effect of predictors on the outcomes, because they do not allow for predictor variables. They can only test the significance of difference between the outcomes.