Histogram binning revisited with a focus on human perception
This addresses the problem of optimizing histogram design for human viewers in data visualization, though it is incremental as it builds on prior binning models.
The paper investigates how well users perceive data distributions from histograms, finding that more bins reduce errors up to a point, and that existing mathematical models generally overestimate the number of bins needed for human perception.
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.