What are the assumptions of normality?

The assumptions of normality are that a sample or population is normally distributed. This means that the mean, median, and mode for the entire population or sample should be equal, and that the data should follow the normal bell-shaped curve. In addition, the assumption of normality also states that the data should be unimodal (i.e., have one peak) and the standard deviation should be equal to the variance. The assumption of normality is important for many statistical tests and procedures; for example, the t-test, analysis of variance (ANOVA), regression analysis, and the like. In short, the assumption of normality states that the data should be symmetric and bell-shaped.