IEEE Trans Vis Comput Graph. 2018 Jan;24(1):446-456. doi: 10.1109/TVCG.2017.2743898. Epub 2017 Aug 29.
People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively.
人们对不确定过程（如科学实验）的结果常常存在错误的直觉。许多不确定性可视化技术假设用户具有相当的统计知识，但即使用户具备这些知识，也已被证明会促使得出错误的结论。主动学习方法已被证明可以改善统计推理，但在科学报告中可视化不确定性时很少应用。我们进行了一项对照研究，以评估一种交互式、图形化的不确定性预测技术在传达实验结果不确定性方面的影响。在使用我们的技术时，用户在查看实验的真实采样分布之前，先勾画出他们对实验效果不确定性的预测。我们发现，让用户图形化地预测实验重复的可能效果是提高人们对新实验重复进行预测能力的有效方法。此外，将不确定性可视化为一组离散的结果，而不是连续的概率分布，可以提高对单个实验抽样分布的回忆能力。我们的工作对各种需要引出人们对概率分布的估计并有效地传达不确定性的应用具有重要意义。
