If data exist on 2 variables (*X* & *Y*), the square of the correlation coefficient is called the “coefficient of determination.” This latter coefficient, if multiplied by 100, indicates the % of variability in either variable that’s associated with (or explained by) variability in the other variable.

For example, if *r* = .80, 64% of the variability in *X* is associated with variability in *Y*. Or, if *r* = –.40, 16% of the variability in *X* is associated with variability in *Y*.

To have at least 50% “explained variability,” the correlation must exceed ±.7071.

This number, .7071, is worth remembering because many researchers report that a correlation is “moderate” or has “medium strength” if *r* is near ±.50. In reality, such correlations are not so strong; they indicate that only about 25% of the variability in *Y* is associated with variability in *X*.