In the area of personnel selection, the gap between research evidence and practice is large, has existed for many years, and have become a research area of its own. One suggested explanation for this massive and persistent gap is the way research evidence is communicated (McGraw & Wong, 1992). For example, sffect size indicators such as correlations are often used when communicating the validity of intelligence tests for predicting job performance. This type of statistics however, are unlikely to clarify the implications of selections decisions on organizational outcomes and they are also commonly interpreted in ways that underestimates practical implications of the effects. To create a better understanding of the practical implications of research results it has been suggested that more non-traditional measures of effect size should be used to communicate research findings to practitioners. This paper describes one such effect size; the Common Language (CL) effect size indicator for correlations (Dunlap, 1994). The paper exemplifies how meta-analytic results from the personnel selection literature can be translated into CL terms with the purpose of increasing the understanding of how evidence-based selection systems should be developed.