Methods for Outlier Detection in Metrological Studies

The article addresses the issue of outliers in metrological measurements, which can significantly distort research results and affect measurement accuracy. Outliers that substantially differ from other data points in a sample seriously threaten the reliability of metrological processes. In previous studies, the Isolation Forest model was applied to detect such outliers, demonstrating its effectiveness under certain conditions. For a deeper understanding and validation of the results, it is necessary to compare this approach with traditional robust methods, such as the Interquartile Range (IQR) and Median Absolute Deviation (MAD), already widely used in metrology.