Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s "raincloud plots." In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually "defend" against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of "defensive" raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.
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