Distance correlation is a new way to measure how related different sets of data are, even if they have different sizes. It helps us see if two sets of data are independent or not. The researchers introduced a new concept called Brownian distance covariance, which is like a more advanced version of the usual covariance we use. They found that Brownian distance covariance is the same as the covariance with respect to Brownian motion. This new method can give us more accurate results compared to the traditional way of measuring relationships between data sets.