In statistics, Mahalanobis distance is a distance measure introduced by P. C. Mahalanobis in 1936. It is based on correlations between variables by which different patterns can be identified and analyzed. It gauges similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. In other words, it is a multivariate effect size.
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“Why does the past look so enticing to us? For the same reason why from a distance a meadow with flowers looks like a flower bed.”
—Franz Grillparzer (1791–1872)