When is it appropriate to use a normal model to model a set of data?

A normal model is appropriate when the data is continuous, symmetrically distributed, and exhibits a bell-shaped curve, typically characterized by a mean and standard deviation. Additionally, the data should ideally meet the conditions of the Central Limit Theorem, where the sample size is sufficiently large (usually n > 30) to ensure that the sampling distribution of the mean approaches normality, regardless of the original distribution. Lastly, it is important to check for the absence of extreme outliers, as they can distort the normality assumption.