In this study age norms were derived empirically, both regression lines being taken into consideration. From the point of view of statistics it becomes imperative, in order to use the technique here advised, to have the average age of a score—since we are going to predict age from score—to translate crude scores into indices of maturity in each subject under consideration. We are in error in the use of grade norms, if we find the average score of a grade and then, when we obtain that score in practice, say that the work is of that grade. To be able to say this we must know the average grade of a score. This takes in an entirely different cross-section of data. If we get the average score of all children in grade 6, then we can predict what a 6th grade child is likely to get, but we can say nothing about a child who is not in grade 6. In order to decide that a 4th grade child has 6th grade ability, we must know that he has such ability that all children who share this score make an average grade of 6.[6] It would be wise then to get the regression of score on age as well as the regression of age on score, since they are not identical, the correlation between score and age being less than unity. We will note in passing that the data to establish these norms, except those of reading, are not as complete as may be desired, inasmuch as it was difficult to get test scores where the age in months also was available. However, the general data behind the grade norms could be used to keep the results from any crude error; and the averages were obtained for every month from 8 years to 14 years, with a corresponding refinement in intervals of score, which made still more improbable an error in the general tendency of the regression lines. Then all the distributions, when grouped by years, were corrected for truncation; that is, the tendency for the brighter children of the older group to be in high school (the data were from elementary schools only) and the duller children of the younger group to be in the lower grades where they could not be reached was recognized and corrected by finding the average, standard deviation, and number of cases which would have existed if these forces of truncation were not operating. This was done by the use of the other one half of the figures comprising Table XI of Pearson’s _Tables for Statisticians and Biometricians_. Dr. Truman L. Kelley pointed the way to its derivation.