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.

中文翻译：

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重新审视直方图分箱，重点关注人类感知

本文提出了一项定量用户研究，以评估用户从直方图表示中视觉感知基础数据分布的能力。我们使用了不同的样本和 bin 大小以及四种不同的分布（均匀分布、正态分布、双峰分布和伽马分布）。研究结果证实，一般来说，更多的 bin 与观看者的错误更少相关。然而，在一定数量的 bin 上，错误率不能通过增加更多的 bin 来改善。通过将我们的研究结果与现有的直方图分箱数学模型的结果（例如 Sturges 公式、Scott 的正常参考规则、Rice 规则或 Freedman-Diaconis 的选择）进行比较，我们可以看到它们中的大多数高估了使分布对人类观察者可见所必需的箱。