Discuss divergence in normality with the help of suitable diagram and describe the factors causing divergence in the normal distribution. Discuss how divergence in normality is measured.

In psychology, statistical analysis is often used to examine patterns, distributions, and behaviors within a set of data. One fundamental concept that frequently arises is the notion of "normality." A normal distribution, often referred to as a bell curve, is a probability distribution that is symmetrical and follows a specific pattern where most of the data points cluster around a central mean, with frequencies tapering off symmetrically in both directions. However, in many psychological studies, the assumption of normality can sometimes be challenged, and data may show divergence from the typical normal distribution. This phenomenon is referred to as divergence in normality, and it can manifest in various ways depending on the nature of the data.

### Divergence from Normality

Divergence from normality refers to deviations from the standard bell-shaped curve. These deviations can appear in different forms, such as skewness (a shift to the left or right of the mean) or kurtosis (the shape of the peak of the distribution). While the normal distribution assumes a perfect symmetry and a specific rate of change, real-world data—particularly in psychology—often does not adhere to these ideal characteristics. 

A common way to visualize a normal distribution is through a graph, where the x-axis represents the range of values (e.g., test scores, reaction times), and the y-axis represents the frequency or probability of each value occurring. In a typical normal distribution, the graph forms a smooth, symmetrical bell shape. When there is divergence from this pattern, the distribution may show:

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