The assumption of normality is especially critical when constructing reference intervals for variables ( 6).
Many of the statistical procedures including correlation, regression, t tests, and analysis of variance, namely parametric tests, are based on the assumption that the data follows a normal distribution or a Gaussian distribution (after Johann Karl Gauss, 1777–1855) that is, it is assumed that the populations from which the samples are taken are normally distributed ( 2- 5). Statistical errors are common in scientific literature, and about 50% of the published articles have at least one error ( 1).
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