Assessing Normality

Assessing normality is a critical aspect of statistical analysis, serving as a fundamental step in various fields such as finance, psychology, and biology. It involves evaluating whether data points follow a normal distribution, which is characterized by a bell-shaped curve. Normality assessment aids researchers and analysts in making accurate inferences and predictions based on the data at hand. Through techniques like histograms, Q-Q plots, and statistical tests such as the Shapiro-Wilk test, analysts can determine if a dataset conforms to the assumptions of normality. Understanding normality is pivotal for robust statistical modeling and decision-making processes.