Small effect sizes can make a big difference ~ Suppose a test is used to select applicants for a job advancement program. Assume that there are two types of applicants (red and blue) and that the mean for the red applicants is 52, the mean for the blue applicants is 50, both distributions are nearly normal, and the standard deviation for each distribution is 10. The difference in means (that is, the effect size) is therefore only 0.2 standard deviations and the effect of being either red or blue would be considered ‘small,’ not visible to the naked eye. At one large company there are two programs that use this test to screen applicants. Program 1 is less selective. Managers use a cutoff value of 40 for this program, meaning that any applicant who scores higher than 40 is accepted into the program. Program 2 is very selective. Managers use a cutoff value of 70 for Program 2, meaning that only applicants who score higher than 70 are accepted into the program.