Fitting Bell Curves to Data Distributions Using Visualization
Fitting Bell Curves to Data Distributions Using Visualization
复制标题
使用可视化将钟形曲线拟合到数据分布
DOI：
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发表时间：
2022
影响因子：
5.2
通讯作者：
N. Elmqvist
中科院分区：
文献类型：
--
作者：
E. Newburger;M. Correll;N. Elmqvist
理想化的概率分布，如正态或其他曲线，是验证性统计检验的根本。但人们对这些理想化曲线的理解程度如何呢？实际上，人类视觉系统是否允许我们将样本数据分布与假设的总体分布进行匹配，这些样本可能是从这些总体分布中提取的？不同的可视化技术对这一能力有何影响？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的能力：条形直方图、点图直方图、条形图和盒图。我们发现，总体估计分布的中心(均值)具有一定的成功率和很小的偏差。我们还发现，人们通常高估了标准差--我们称之为“保护伞效应”，因为人们倾向于用曲线覆盖整个分布，就好像是在遮挡上面的天堂--而带状曲线图的精确度最高。
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 article 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.
DOI：
10.1109/tvcg.2020.3030429
发表时间：
2020-07
影响因子：
5.2
作者：
Brian D. Ondov;Fumeng Yang;M. Kay;N. Elmqvist;S. Franconeri
通讯作者：
Brian D. Ondov;Fumeng Yang;M. Kay;N. Elmqvist;S. Franconeri
DOI：
10.1109/tvcg.2020.3030335
发表时间：
2021-02-01
影响因子：
5.2
作者：
Kale, Alex;Kay, Matthew;Hullman, Jessica
通讯作者：
Hullman, Jessica