Why is Non-Normal Distribution Important in Epidemiology?
Non-normal distribution is important because traditional statistical tests that assume normality (e.g., t-tests, ANOVA) may not be valid if the data is non-normal. This can lead to incorrect conclusions. Recognizing non-normal distribution allows epidemiologists to choose appropriate statistical methods, such as non-parametric tests or data transformation techniques, ensuring more reliable and accurate results.
