What is the main goal of analyzing outliers in a dataset?

The primary goal of analyzing outliers in a dataset is to ensure data accuracy and insight quality. Outliers can skew results, affecting statistical measures like the mean and potentially leading to misleading conclusions if not properly understood. By identifying and analyzing these anomalies, analysts can determine whether they represent genuine variations in the data or errors that need to be addressed. This thorough investigation helps maintain the integrity of the dataset and enhances the reliability of the insights derived from it, ensuring that business decisions based on the data are sound and well-founded.

Analyzing outliers also provides opportunities to uncover valuable insights, as sometimes these unusual values can indicate underlying trends, patterns, or specific events that merit further investigation. The focus is on understanding the root causes of the outliers, which ultimately contributes to better data quality and analytic outcomes.