A Novel Normality Test Using an Identity Transformation of the Gaussian Function

Oguz Akbilgic, J. Andrew Howe

Abstract

Normality is the most frequently required assumption for statistical techniques. Thus, evaluation of the normality assumption is the first step of many statistical analyses. Although there are many normality tests in the literature, none dominate for all conditions. This paper introduces a novel normality test, and its performance is compared with some of the other normality tests via a Monte Carlo simulation study. Tests are evaluated according to the Type I error and Power.

Keywords

normality test; Gaussian distribution; probability distributions

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