The study compared two methods for measuring correlation between data points: Pearson’s correlation coefficient and An Absolute Value correlation coefficient. The researchers found that An Absolute Value correlation coefficient is more accurate and robust when dealing with outliers in the data. This means it gives a better estimate of how related two sets of data are, even when there are unusual data points that could skew the results. Overall, An Absolute Value correlation coefficient is a better choice for calculating correlation in the presence of outliers.