Outlier detection is a technique used in data analysis to identify data points that differ significantly from the rest of the dataset. These unusual points, known as outliers, can indicate errors, rare events, or unique insights. By recognizing these anomalies, analysts can improve the quality of their data and make more informed decisions. In various fields like finance, healthcare, and machine learning, outlier detection helps in spotting fraud, diagnosing diseases, or enhancing model accuracy. Techniques such as statistical tests, clustering, and machine learning algorithms are commonly employed to effectively identify and handle these outliers, ensuring better data integrity and analysis outcomes.