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：数据可视化中不确定性的更好表达是近期研究活动的焦点。当前文献存在一个问题，即对不确定性定义及其在图表中表达的含义缺乏清晰性。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性表达有效性的实验中。在本综述中，我们总结了当前文献，提供了可用的定义，并通过示例说明了这些定义。在此过程中，我们探讨了在统计图形中实现透明性真正需要付出怎样的努力。希望本综述能对指导新的图形方法和实验研究有所裨益。