Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.
This visualization illustrates the appropriate tests to use when your research hypothesis is that two treatments are equally effective, or that a new treatment is no worse than the current gold standard.
Here's a new visualization that shows the p-curve distribution when comparing the means of two independent samples for varying effects. Many know that the distribution is uniform when the null is true, but what about when it isn't?