PREDICTION OF VOCATIONAL SUCCESS 191
score. The range marked off by the critical score is called a
preferred range or a critical section.
For instance, an intelligence test score of C + on Army
Alpha is a critical score for success in a business career,
because the proportion of men who become successes in busi-
ness is small among men who score lower than this.
For inspectors in one great factory a score of 70 in Army
Alpha was found to be a critical score. Of 337 men who in
two successive years were trained to this work, 224 were
satisfactory. Of the 281 men who scored 70 or better, 75%
were satisfactory, while of the 56 men who scored below 70,
only 23% proved satisfactory. In other words, the pre-
ferred range for men to be trained as inspectors in that
plant is 70 or above.
Often the preferred range lies between two critical scores,
because workers whose abilities are above a certain maxi-
mum score are soon dissatisfied, and leave before they have
made good. It is quite true of salesmen for certain products
and of operatives on certain machine processes that ‘the
brighter they are the quicker they leave.” For clerical em-
ployees doing purely routine work there is one preferred
range, while another range is preferred for jobs where some
independent judgment or initiative is of value.
The best procedure in establishing critical scores is to plot
scores made by members of the two groups—successes and
failures—on the same chart, indicating successes by one
kind of symbol and failures by another kind. If there is an
intermediate group, a third symbol may be used. The in-
vestigator may then by inspection easily find the sections of
the distribution in which one group is represented in rela-
tively greater proportion than the other groups. These
critical sections are then set off by critical scores. They
should include a large proportion of all the cases.
Critical scores may also be determined by the use of the
formula for Pearson biserial 7 (formula 22).
The significance of critical sections may also be deter-
mined by considering the alternative categories assumed in
