  This paper presents a quantitative user study to evaluate how well users can
visually perceive the underlying data distribution from a histogram
representation. We used different sample and bin sizes and four different
distributions (uniform, normal, bimodal, and gamma). The study results confirm
that, in general, more bins correlate with fewer errors by the viewers.
However, upon a certain number of bins, the error rate cannot be improved by
adding more bins. By comparing our study results with the outcomes of existing
mathematical models for histogram binning (e.g., Sturges' formula, Scott's
normal reference rule, the Rice Rule, or Freedman-Diaconis' choice), we can see
that most of them overestimate the number of bins necessary to make the
distribution visible to a human viewer.
