Better representation of the uncertainty in a data visualisation is a focus of recent research activity. A problem with the current literature is that there is a lack of clarity about the definition of uncertainty and what it means to represent it in a plot. This confusion results in a significant amount of conflicting results in the literature, especially in experiments that assess the effectiveness of different uncertainty representations. In this review, we summarise the current literature, provide workable definitions, and illustrate these definitions with examples. In doing so, we ask what it really takes to achieve transparency in statistical graphics. It is hoped that it will be useful for guiding new graphics methodology and experimental research.

翻译：更好地表示数据可视化中的不确定度是近期研究活动的焦点。当前文献存在一个问题，即关于不确定度的定义以及在图表中表示它的含义缺乏清晰性。这种混淆导致文献中出现大量相互矛盾的结果，尤其是在评估不同不确定度表示方法有效性的实验中。在本综述中，我们总结了当前文献，提供了可操作的定义，并通过示例说明这些定义。在此过程中，我们探讨了实现统计图形透明度真正需要什么。希望这篇综述能对指导新的图形方法和实验研究有所裨益。