Statistical graphics play a crucial role in exploratory data analysis, model checking, and diagnosis. The lineup protocol enables statistical significance testing of visual findings, bridging the gulf between exploratory and inferential statistics. In this article, inferential methods for statistical graphics are developed further by refining the terminology of visual inference and framing the lineup protocol in a context that allows direct comparison with conventional tests in scenarios when a conventional test exists. This framework is used to compare the performance of the lineup protocol against conventional statistical testing in the scenario of fitting linear models. A human subjects experiment is conducted using simulated data to provide controlled conditions. Results suggest that the lineup protocol performs comparably with the conventional tests, and expectedly outperforms them when data are contaminated, a scenario where assumptions required for performing a conventional test are violated. Surprisingly, visual tests have higher power than the conventional tests when the effect size is large. And, interestingly, there may be some super-visual individuals who yield better performance and power than the conventional test even in the most difficult tasks. Supplementary materials for this article are available online.
More detail can easily be written here using Markdown and $\rm \LaTeX$ math code.