Aldor-Noiman, SivanBrown, Lawrence DStine, Robert ABuja, AndreasRolke, Wolfgang2023-05-232023-05-232013-01-012016-07-18https://repository.upenn.edu/handle/20.500.14332/47872Many statistical procedures assume the underlying data generating process involves Gaussian errors. Among the well-known procedures are ANOVA, multiple regression, linear discriminant analysis and many more. There are a few popular procedures that are commonly used to test for normality such as the Kolmogorov-Smirnov test and the ShapiroWilk test. Excluding the Kolmogorov-Smirnov testing procedure, these methods do not have a graphical representation. As such these testing methods offer very little insight as to how the observed process deviates from the normality assumption. In this paper we discuss a simple new graphical procedure which provides confidence bands for a normal quantile-quantile plot. These bands define a test of normality and are much narrower in the tails than those related to the Kolmogorov-Smirnov test. Correspondingly the new procedure has much greater power to detect deviations from normality in the tails.This is an Accepted Manuscript of an article published by Taylor & Francis in The American Statistician on 11 Oct 2013, available online: http://wwww.tandfonline.com/10.1080/00031305.2013.847865.confidence bandsgraphical presentationnormality testpower analysisquantile-quantile plotStatistics and ProbabilityArticle