We unpack how the Mahalanobis distance generalizes simple Z-scores to high‑dimensional, correlated data by using the inverse covariance to whiten the data. Instead of a naive Euclidean ball, data lie in an ellipsoid shaped by correlations; distance is directional and scale‑aware, turning chaos into a single, unitless score. Learn how this helps detect multivariate outliers, measure market turbulence, and power clustering, classification, and fraud detection. Originating with Prasanta Chandra Mahalanobis in 1936 for skull measurements, this metric remains a cornerstone of modern data analysis—and a bridge to smarter AI. Sponsored by EmberSILK.

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Sponsored by Embersilk LLC