We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how well people are able to visually validate models, and how their performance compares to visual and computational estimation. As a starting point, we conducted a study across two populations (crowdsourced and volunteers). Participants had to both visually estimate (i.e, draw) and visually validate (i.e., accept or reject) the frequently studied model of averages. Across both populations, the level of accuracy of the models that were considered valid was lower than the accuracy of the estimated models. We find that participants' validation and estimation were unbiased. Moreover, their natural critical point between accepting and rejecting a given mean value is close to the boundary of its 95% confidence interval, indicating that the visually perceived confidence interval corresponds to a common statistical standard. Our work contributes to the understanding of visual model validation and opens new research opportunities.

翻译：我们研究个体在视觉上验证统计模型与数据拟合程度的能力。尽管视觉模型估计已被广泛研究，但视觉模型验证仍缺乏深入探讨。尚不清楚人们视觉验证模型的准确程度，以及其表现与视觉估计和计算估计的比较情况。作为研究起点，我们在两个群体（众包用户与志愿者）中进行了一项实验。参与者需要同时进行视觉估计（即绘制）和视觉验证（即接受或拒绝）常用的平均值模型。在两个群体中，被视为有效的模型其准确率均低于估计模型的准确率。我们发现参与者的验证和估计均无偏差。此外，他们接受或拒绝给定平均值的自然临界点接近其95%置信区间的边界，这表明视觉感知的置信区间与常见统计标准一致。本研究有助于理解视觉模型验证机制，并开辟了新的研究方向。