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![Schematic diagram of outliers in bivariate normal data. The point 'A' has large univariate z scores but a small Mahalanobis distance. The point 'B' has a large Mahalanobis distance. Only 'b' is a multivariate outlier.](https://blogs.sas.com/content/iml/files/2019/03/MVOutliers1.png)
An important concept in multivariate statistical analysis is the Mahalanobis distance. The Mahalanobis distance provides a way to measure how far away an observation is from the center of a sample while accounting for correlations in the data. The Mahalanobis distance is a good way to detect outliers in multivariate