Evaluating agreement between two measurements requires more than correlation; Pearson correlation captures whether two variables move together in a linear fashion, but it doesn’t tell you whether the actual values match across methods. Two methods can be perfectly correlated yet disagree by a consistent bias or a scaling difference, so they don’t agree even though their relationship is linear. The concordance correlation coefficient addresses this by combining two ideas: precision and accuracy. Precision mirrors the association part—how tightly the data cluster around the best-fit line. Accuracy brings in how close that line is to the line of identity (the 45-degree line where x equals y). If there’s systematic bias (one method consistently too high or too low) or a difference in scale, the CCC decreases because it penalizes both lack of fit to the identity line and differences in spread between the methods. So it’s correct that Pearson measures linear association (not agreement) while the concordance correlation coefficient accounts for both scale/level differences and agreement between the methods.