 However, the (log-)odds ratio, a popular measure of effect size in epidemiological research, shows a deviant behavior. Uniformly assigning individuals to treatment and control condition reliably produces the AMP for the odds ratio whenever the third variable (=the subpopulation attribute) influences the success rate, given the treatment level (Theorem 2.4, Samuels 1993). The odds ratio is thus a particularly tricky association measure. Greenland (1987) gives the instructive example of an odds ratio that is equal in all subpopulations with row-uniform design, but halved when data are pooled.