Distribution-based methods

Distribution-based methods analyze data by examining how values are spread or distributed across a dataset. They focus on the overall pattern, such as where most data points fall, the range of values, and how they cluster. These methods help identify typical behaviors, outliers, or unusual patterns by understanding the underlying distribution. For example, they can reveal if data follows a normal (bell-shaped) pattern or another type of distribution. In summary, distribution-based methods provide insights by studying the structure and spread of data, helping in decision-making, anomaly detection, and understanding data characteristics.