<p>Displaying the component-wise between-group differences high-dimensional datasets is problematic because widely used plots such as Bland–Altman and Volcano plots do not show what they are colloquially <i>believed</i> to show. Thus, it is difficult for the experimentalist to grasp why the between-group difference of one component is “significant” while that of another component is not. Here, we propose a type of “Effect Plot” that displays between-group differences in relation to respective underlying variability for every component of a high-dimensional dataset. We use synthetic data to show that such a plot captures the essence of what determines “significance” for between-group differences in each component, and provide guidance in the interpretation of the plot. Supplementary online materials contain the code and data for this article and include simple R functions to produce an effect plot from suitable datasets.</p
