The study compared two statistical methods to analyze data from repeated measurements in experiments. They found that using a simpler method for certain effects like time and interaction gave better results than a more complex method. The researchers also discovered that using the correct covariance structure in the analysis led to higher estimated powers for treatment effects. Overall, the study showed that choosing the right statistical approach can improve the accuracy of results in repeated measures designs.