Date of this Version
The American Statistician
Many 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 bands, graphical presentation, normality test, power analysis, quantile-quantile plot
Aldor-Noiman, S., Brown, L. D., Stine, R. A., Buja, A., & Rolke, W. (2013). The Power to See: A New Graphical Test of Normality. The American Statistician, 67 (4), 249-260. http://dx.doi.org/10.1080/00031305.2013.847865
Date Posted: 27 November 2017