We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.

翻译：我们开展了一项关于COVID-19病例数据可视化的受控众包实验，旨在研究不同的绘图方法、时间窗口以及数据特性是否以及如何影响人们对真实世界COVID-19数据的解读，以及人们对数据未来演变趋势的预测。研究发现，与未经平滑处理的柱状图数据相比，7日反向移动平均平滑曲线能有效降低周期性数据模式造成的干扰。此外，我们还发现平滑曲线的存在有助于读者就数据的未来演变趋势形成共识。同时，固定7日平滑窗口会导致数据中感知到的重复模式数量因绘图时间区间的不同而产生差异——这表明根据绘图窗口大小动态调整平滑窗口大小，可能是一种影响图中伪模式感知的有效策略。