Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of slope (i.e., accepting a shown line). Notably, we found bias toward slopes that are "too steep" in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

散点图中的线性回归模型的视觉验证常被用于评估模型质量，但其效力仍未被量化。本研究通过两个实证实验，调查了个体验证线性回归模型（线性趋势）的能力，并研究了常见可视化设计对验证质量的影响。实验结果显示，视觉估计斜率（即拟合线到数据）的准确度高于斜率的视觉验证（即接受展示的线）。值得注意的是，在这两种情况下，都存在对“过陡”斜率的偏见。这带来了新的发现，参与者自然而然地通过点与线之间的正交距离（即ODR回归）评估回归，而不是常见的垂直距离（OLS回归）。在第二个实验中，我们探究了对回归可视化进行常见设计（误差线、边界框和置信区间）是否能提高视觉验证。尽管误差线降低了验证偏见，但结果未显示出任何设计的准确性改进。总的来说，我们的研究结果表明，在散点图中使用线性趋势的视觉模型验证时应谨慎。

警惕凭肉眼验证：散点图中线性趋势的视觉验证