Introduction Testing for normality is a critical step in data analysis, especially when using statistical methods that assume a normal distribution. This blog provides a comprehensive overview of several statistical tests used to assess the normality of your data, helping you ensure the validity of your analyses. What is a Normal Distribution? A normal distribution, or Gaussian distribution, is a bell-shaped curve that is symmetrical around the mean. Key characteristics include: Symmetry: The left and right sides of the curve are mirror images. Central peak: Most of the data points are concentrated around the mean. Tails: The tails approach, but never touch, the horizontal axis. Empirical Rule: Approximately 68% of data falls within one standard deviation of the mean, 95% within two, and 99.7% within three. Why Test for Normality? Many statistical methods, such as t-tests, ANOVA, and linear regression, assume that the data follow a normal distribution. Using these methods on non-no