Shlomo S. Sawilowsky, a professor at Wayne State University, proposes expanding Cohen's (1988) rules of thumb for interpreting effect sizes to include very small, very large, and huge effect sizes. The article discusses the reasons for this expansion and its implications for designing Monte Carlo studies. Sawilowsky emphasizes the importance of avoiding true random number generators in simulations that require replication and ensuring that study parameters are comprehensive. He suggests that effect size parameters should not be limited to the minimum and maximum values of .2 and .8, as current research findings indicate the existence of much larger effect sizes. The revised rules of thumb are defined as follows: d (.01) = very small, d (.2) = small, d (.5) = medium, d (.8) = large, d (1.2) = very large, and d (2.0) = huge. This expansion aims to provide a more accurate and comprehensive framework for interpreting effect sizes in statistical analyses.Shlomo S. Sawilowsky, a professor at Wayne State University, proposes expanding Cohen's (1988) rules of thumb for interpreting effect sizes to include very small, very large, and huge effect sizes. The article discusses the reasons for this expansion and its implications for designing Monte Carlo studies. Sawilowsky emphasizes the importance of avoiding true random number generators in simulations that require replication and ensuring that study parameters are comprehensive. He suggests that effect size parameters should not be limited to the minimum and maximum values of .2 and .8, as current research findings indicate the existence of much larger effect sizes. The revised rules of thumb are defined as follows: d (.01) = very small, d (.2) = small, d (.5) = medium, d (.8) = large, d (1.2) = very large, and d (2.0) = huge. This expansion aims to provide a more accurate and comprehensive framework for interpreting effect sizes in statistical analyses.