How to handle outliers in data?

Outlier handling involves identifying and addressing anomalous data points that deviate significantly from other observations within a dataset, either through transformation, correction, or removal. This process ensures statistical analyses and model training are robust and not unduly influenced by extreme values. Key principles necessitate initial identification through visualization (e.g., box plots, scatter plots) or statistical methods (e.g., Z-scores, IQR-based thresholds). Deciding how to handle outliers depends critically on the cause and the analysis goal. Domain knowledge is essential to distinguish true anomalies from meaningful extreme values before applying techniques like capping, winsorizing, imputation, or deletion. Considerations must include the potential impact on statistical distribution, model assumptions, and avoiding the inappropriate removal of valid information characterizing the underlying phenomenon. Implementation involves sequential steps. First, detect outliers using appropriate methods. Second, investigate their potential sources and validity using domain expertise. Third, select and apply a suitable treatment strategy based on the investigation and analysis objectives. Fourth, conduct the analysis with the treated data. Finally, compare results with the original dataset analysis to assess the sensitivity and impact of the chosen outlier handling approach on the conclusions. This structured process enhances result reliability.