Normality tests are statistical procedures used to determine if a dataset follows a normal distribution, which is a common assumption in many statistical analyses. These tests help researchers assess whether their data can be treated as normally distributed, which is important for the validity of various statistical methods. Common normality tests include the Shapiro-Wilk test, Kolmogorov-Smirnov test, and Anderson-Darling test. Each test evaluates the data's distribution and provides a p-value, indicating whether to reject the null hypothesis that the data is normally distributed. Understanding normality is crucial for accurate data interpretation and analysis.