Mastering Outlier Detection: The Complete Guide to the Interquartile Range (IQR) Method, Tukey’s Fences, Python Code, and Automated Tools
I n data science, statistics, and machine learning, raw datasets are rarely pristine. They are messy, unpredictable, and frequently plagued by anomalies, measurement errors, and extreme values. These anomalies—commonly known as outliers —can drastically skew summary statistics, violate the assumptions of parametric statistical tests, and degrade the predictive performance of machine learning models. Among the various techniques used to spot and handle anomalies, the Interquartile Range (IQR) method (also known as Tukey’s Fences ) stands out as one of the most robust, non-parametric approaches. Unlike the standard deviation method, which relies on the mean and is itself sensitive to extreme values, the IQR method uses robust statistics (medians and quartiles) that resist distortion. In this comprehensive guide, we will explore the theory behind quartiles, look at how Tukey’s fences operate mathematically, examine subtle edge cases and quartile calculation gotchas, review programmatic i...