Assumptions of normality

The assumption of normality means that when we analyze data, we expect the data points to follow a bell-shaped pattern, where most values cluster around the average, with fewer at the extremes. This pattern, called a normal distribution, helps ensure that statistical tests produce accurate results. If data significantly deviate from this shape, the conclusions from these tests might be unreliable. Normality is important because many statistical methods rely on this pattern to make valid inferences about the data and the relationships being studied.