What does outlier detection aim to accomplish?

Outlier detection primarily focuses on identifying data points that significantly differ from the majority of the data set. This concept is crucial because outliers can represent anomalies or variations that may indicate important insights, errors, or unique occurrences within the data. By detecting these outliers, analysts can better understand the underlying patterns, potential issues, or unique cases that may warrant special consideration or further investigation.

In practical terms, recognizing outliers can help in refining data models, as they might skew results or lead to misleading conclusions if not addressed properly. Effectively managing outliers aids in achieving more accurate and reliable analyses, ultimately leading to better-informed decisions based on the data.

This understanding is distinct from other goals, such as improving overall data accuracy, reducing complexity, or solely analyzing trends over time, which may encompass broader data management or analysis objectives that do not focus specifically on the unique insights provided by outlier detection.