使用可视化技术将钟形曲线拟合到数据分布
Fitting Bell Curves to Data Distributions using Visualization
AI总结：
本研究通过众包实验测试人们在四类可视化图表中拟合正态曲线的能力，发现均值估计偏差小、标准差常被高估（雨伞效应），且带状图拟合准确度最高。
摘要：
正态分布或其他曲线这类理想化概率分布是验证性统计检验的基础。但人们对这些理想化曲线的理解程度如何？具体而言，人类视觉系统能否让我们将样本数据分布与可能生成这些样本的假设总体分布相匹配？不同的可视化技术又会对这种能力产生怎样的影响？本文分享了一项众包实验的结果，该实验测试了受访者将正态曲线拟合到四种不同数据分布可视化形式的能力：条形直方图、点图直方图、带状图和箱线图。我们发现，众包参与者能够较为成功地估计分布的中心（均值），且偏差很小。我们还发现，人们通常会高估标准差，我们将其称为“雨伞效应”——因为人们倾向于用曲线覆盖整个分布，仿佛要为它遮风挡雨；此外，带状图能带来最高的拟合准确度。
英文摘要：
Idealized probability distributions, such as normal or other curves, lie at the root of confirmatory statistical tests. But how well do people understand these idealized curves? In practical terms, does the human visual system allow us to match sample data distributions with hypothesized population distributions from which those samples might have been drawn? And how do different visualization techniques impact this capability? This paper shares the results of a crowdsourced experiment that tested the ability of respondents to fit normal curves to four different data distribution visualizations: bar histograms, dotplot histograms, strip plots, and boxplots. We find that the crowd can estimate the center (mean) of a distribution with some success and little bias. We also find that people generally overestimate the standard deviation, which we dub the "umbrella effect" because people tend to want to cover the whole distribution using the curve, as if sheltering it from the heavens above, and that strip plots yield the best accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
