The lab shows that the sample mean is not always the best estimator. This allows the user to see the effect of changing significance level and sample size on the width of intervals as well as the effect of violation of assumptions on the confidence interval coverage probability. It draws a graph with the test statistic marked and the tail areas corresponding to the p -value shaded in. This allows the user to see the effect of sample size and degree of falseness on the power of the test. The first set of four samples are from normal populations having differing means, while in the second set the population means are all the same.
All populations have the same variance.
This allows the user to see how the test is comparing the between sample variability to the within sample variability. This allows users to see how nonuniform the placement of points can look even though they are in fact being uniformly placed.