效应大小判断和决策的视觉推理策略
Visual Reasoning Strategies for Effect Size Judgments and Decisions
摘要：
不确定性可视化通常强调点估计，以通过视觉比较来支持量级估计或决策。然而，当设计选择强调手段时，用户可能会忽略不确定性信息，并将视觉距离误解为效果大小的代理。我们提出了一个混合设计实验的结果，测试8个不确定性可视化设计：95%的遏制间隔，假设的结果图，密度，和分位数点图，每一个都有和没有手段增加。我们发现，增加手段的不确定性可视化的幅度估计和决策有小的偏差影响，与贴现不确定性。我们还看到，支持最小偏差效应大小估计的可视化设计不支持最佳决策，这表明图表用户对效应大小的感觉可能不一定是相同的，当他们使用相同的信息进行不同的任务。在对用户策略描述的定性分析中，我们发现许多用户会转换策略，并且在存在最优策略时不会使用最优策略。不确定性可视化，这是最佳的设计在理论上可能不是最有效的，在实践中，因为用户满意的方式与proxistics，建议的机会，以更好地了解可视化的有效性，通过建模的潜在策略集。
英文摘要：
Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.
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。
