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.

翻译：回归专家一致建议使用残差图进行模型诊断，尽管已有许多数值假设检验程序旨在利用残差评估模型拟合问题。本文通过视觉推断实验数据，为这一建议提供依据。我们展示了传统检验过于敏感，这往往导致"模型拟合不足"的结论过于频繁。该实验采用排列协议，将残差图置于零假设图的背景下，有助于对残差图进行可靠且一致的解读，从而实现更好的模型诊断。此外，在传统检验因数据污染而无法检测模型问题的相反情形中，该协议同样适用。排列协议还能同时检测与良好残差特征的多重偏离。