Statistical anomalies are unexpected results or patterns that deviate significantly from what is considered normal or typical in a dataset. For example, if a sports team usually scores between 50 and 70 points per game but suddenly scores 120 points in one game, that performance would be a statistical anomaly. These anomalies can indicate unusual events, errors in data collection, or new trends that need further investigation. Identifying statistical anomalies is important in various fields, including finance, healthcare, and scientific research. By recognizing these outliers, analysts can make better decisions, improve predictions, and uncover hidden insights that might otherwise go unnoticed.