2002
DOI: 10.1146/annurev.publhealth.23.100901.140546
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The Importance of the Normality Assumption in Large Public Health Data Sets

Abstract: It is widely but incorrectly believed that the t-test and linear regression are valid only for Normally distributed outcomes. The t-test and linear regression compare the mean of an outcome variable for different subjects. While these are valid even in very small samples if the outcome variable is Normally distributed, their major usefulness comes from the fact that in large samples they are valid for any distribution. We demonstrate this validity by simulation in extremely non-Normal data. We discuss situatio… Show more

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Cited by 1,301 publications
(949 citation statements)
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“…No colinearity or interaction was found with regard to the variables. For all of the analyses, robust estimates were used to account for influential outliers or non-constant variance (Lumley et al 2002).…”
Section: Resultsmentioning
confidence: 99%
“…No colinearity or interaction was found with regard to the variables. For all of the analyses, robust estimates were used to account for influential outliers or non-constant variance (Lumley et al 2002).…”
Section: Resultsmentioning
confidence: 99%
“…The serum levels of the immune markers were entered into the logistic models as tertiles. For all of the analyses, robust estimates were used due to the nonconstant variance (Lumley et al 2002).…”
Section: Resultsmentioning
confidence: 99%
“…Based on the large sample size, we assumed normality [14] and used parametric statistical tests. We decided, a priori, that a difference of 10% between the NHDS and NIS was meaningful.…”
Section: Methodsmentioning
confidence: 99%