A statistical anomaly refers to a data point or a set of data that deviates significantly from the expected pattern or trend. These anomalies can indicate unusual behavior or events that may warrant further investigation. For example, if a weather station records an unusually high temperature compared to historical averages, it could be considered a statistical anomaly. Identifying statistical anomalies is important in various fields, including finance, healthcare, and quality control. By recognizing these outliers, analysts can uncover potential issues, such as fraud, disease outbreaks, or defects in manufacturing processes, leading to more informed decision-making.