Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
SummaryWe consider a graphical approach to comparing multiple treatments that allows users to easily infer differences between any treatment effect and zero, and between any pair of treatment effects. This approach makes use of a flexible, resampling-based procedure that asymptotically controls the familywise error rate (the probability of making one or more spurious inferences). We demonstrate the usefulness of this approach with three empirical examples.