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：本文研究了个人在数据拟合方面对统计模型进行视觉验证的能力。尽管视觉模型估计已得到广泛研究，但视觉模型验证仍处于探索不足的状态。人们能否有效进行视觉模型验证，以及其表现与视觉估计和计算估计相比如何，目前尚不明确。作为起点，我们在两个群体（众包参与者与志愿者）中开展了一项研究。参与者需同时进行视觉估计（即绘制）和视觉验证（即接受或拒绝）常见的平均值模型。在两个群体中，被认为有效的模型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是无偏的。此外，他们在接受或拒绝给定平均值时的自然临界点接近于该值的95%置信区间边界，表明视觉感知到的置信区间与常见的统计标准相符。本研究有助于理解视觉模型验证，并开辟了新的研究方向。