Visualization supports exploratory data analysis (EDA), but EDA frequently presents spurious charts, which can mislead people into drawing unwarranted conclusions. We investigate interventions to prevent false discovery from visualized data. We evaluate whether eliciting analyst beliefs helps guard against the over-interpretation of noisy visualizations. In two experiments, we exposed participants to both spurious and 'true' scatterplots, and assessed their ability to infer data-generating models that underlie those samples. Participants who underwent prior belief elicitation made 21% more correct inferences along with 12% fewer false discoveries. This benefit was observed across a variety of sample characteristics, suggesting broad utility to the intervention. However, additional interventions to highlight counterevidence and sample uncertainty did not provide significant advantage. Our findings suggest that lightweight, belief-driven interactions can yield a reliable, if moderate, reduction in false discovery. This work also suggests future directions to improve visual inference and reduce bias. The data and materials for this paper are available at https://osf.io/52u6v/

翻译：可视化支持探索性数据分析（EDA），但EDA经常呈现虚假图表，可能误导人们得出无根据的结论。本研究探讨了防止可视化数据导致假阳性发现的干预措施。我们评估了激发分析师信念是否有助于防范对噪声可视化数据的过度解读。通过两项实验，我们让参与者同时接触虚假和真实的散点图，并评估他们推断这些样本背后数据生成模型的能力。事先经历信念激发的参与者的正确推断率提高了21%，同时假阳性发现减少了12%。这种效益在各种样本特征下均得到体现，表明该干预措施具有广泛适用性。然而，突出反证和样本不确定性的额外干预措施并未带来显著优势。我们的研究结果表明，轻量级的信念驱动交互能够可靠地（尽管程度适中）降低假阳性发现。这项工作还指出了改进视觉推断和减少偏差的未来方向。本文的数据和材料可在 https://osf.io/52u6v/ 获取