A range of charts with different strengths and weaknesses exists to support the visual analysis of univariate distributions, with a limited understanding of which charts best support which tasks and users, and how practitioners use charts. We categorize the available charts for univariate distributions into four groups and present the results of a mixed-methods comparison (n=215) of participants' perception and preferences across boxplots, violinplots, jittered stripplots, and histograms as representatives of their respective categories. The click-to-select approach in our study, combined with data on participants' subjective experiences and preferences, allows to both measure accuracy on benchmark tasks and discuss participants' choices qualitatively. Our analysis reveals differences between charts in task accuracy, common misunderstandings, and preferences across various low-level tasks, and indicates that chart preference and familiarity do not necessarily align with participants' task performance. Interviews with five visualization practitioners further reveal that charts widely preferred by general audiences (such as histograms) or commonly used in scientific domains (such as boxplots) are not inherently the most effective for all tasks.

翻译：为支持单变量分布的可视化分析，存在多种具有不同优势与局限的图表类型，然而目前尚不明确哪些图表最适合特定任务与用户群体，以及实践者如何实际运用这些图表。我们将现有的单变量分布图表分为四类，并以箱线图、小提琴图、抖动散点条图和直方图作为各类别代表，开展混合方法比较研究（n=215），考察参与者的感知与偏好。本研究采用点击选择方法，结合参与者主观体验与偏好数据，既能在基准任务中准确测量表现，又能定性分析参与者的选择逻辑。分析表明，不同图表在任务准确率、常见误解及各层级任务偏好上存在差异，且图表偏好与熟悉程度未必与任务表现一致。通过与五位可视化从业者的访谈进一步揭示，大众广泛偏好的图表（如直方图）或科学领域常用图表（如箱线图）并非在所有任务中天然最优。