Following on from my previous blopig post, Garrett gave the very helpful suggestion of combining Multiple Comparisons Similarity (MCSim) plots to reduce information redundancy. For example, this an MCSim plot from my previous blog post:

This plot shows effect sizes from a statistical test (specifically Tukey HSD) between mean absolute error (MAE) scores for different molecular featurization methods on a benchmark dataset. Red shows that the method on the y-axis has a greater average MAE score than the method on the x-axis; blue shows the inverse. There is redundancy in this plot, as the same information is displayed in both the upper and lower triangles. Instead, we could plot both the effect size and the p-values from a test on the same MCSim.