Regression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems with a model fit. Here we provide evidence for why this is good advice using data from a visual inference experiment. We show how conventional tests are too sensitive, which means that too often the conclusion would be that the model fit is inadequate. The experiment uses the lineup protocol which puts a residual plot in the context of null plots. This helps generate reliable and consistent reading of residual plots for better model diagnosis. It can also help in an obverse situation where a conventional test would fail to detect a problem with a model due to contaminated data. The lineup protocol also detects a range of departures from good residuals simultaneously. Supplemental materials for the article are available online.

翻译：回归专家一致建议在模型诊断中绘制残差图，尽管已有许多利用残差评估模型拟合问题的数值假设检验方法。本文通过视觉推断实验的数据，论证了这一建议的合理性。我们展示了传统检验过于敏感的问题，即其往往得出模型拟合不足的结论。实验采用排阵协议，将残差图置于空白图的背景中，有助于对残差图进行可靠且一致的解读，从而实现更优的模型诊断。该方法还能处理传统检验因数据污染而未能检测出模型问题的相反情况。此外，排阵协议可同时检测残差偏离理想状态的多种表现。本文的补充材料可在网上获取。