publication . Other literature type . Article . 2002

The importance of the normality assumption in large public health data sets.

Thomas Lumley; Paula Diehr; Scott Emerson; Lu Chen;
Restricted
  • Published: 01 May 2002
  • Publisher: Annual Reviews
Abstract
■ 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 situations in which in other methods such as the Wilcoxon rank sum test and ordinal logistic regression (proportional odds model) have been recommended,...
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue