The article discusses the need to expand Cohen's (1988) rules of thumb for interpreting effect sizes to include very small, very large, and huge effect sizes. The author argues that Monte Carlo studies should consider these new effect sizes to ensure comprehensive study parameters. The article also highlights the limitations of true random number generators in simulations requiring replication and the importance of using appropriate random number generators. Cohen originally defined small, medium, and large effect sizes as d = .2, .5, and .8, respectively. However, the author notes that these values have become de facto standards and are often ignored in modern research. The author suggests expanding the effect size rules to include 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 is necessary to account for effect sizes that have been observed in various studies, such as those involving mentoring as an instructional strategy, which have reported effect sizes as large as 2.0. The author also discusses the implications of using these new effect sizes in Monte Carlo studies, emphasizing the need for comprehensive study parameters and the importance of considering a wide range of effect sizes when evaluating the robustness and power of statistical tests. The article concludes that the revised effect size rules should be incorporated into the design of Monte Carlo studies to ensure more accurate and comprehensive results.The article discusses the need to expand Cohen's (1988) rules of thumb for interpreting effect sizes to include very small, very large, and huge effect sizes. The author argues that Monte Carlo studies should consider these new effect sizes to ensure comprehensive study parameters. The article also highlights the limitations of true random number generators in simulations requiring replication and the importance of using appropriate random number generators. Cohen originally defined small, medium, and large effect sizes as d = .2, .5, and .8, respectively. However, the author notes that these values have become de facto standards and are often ignored in modern research. The author suggests expanding the effect size rules to include 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 is necessary to account for effect sizes that have been observed in various studies, such as those involving mentoring as an instructional strategy, which have reported effect sizes as large as 2.0. The author also discusses the implications of using these new effect sizes in Monte Carlo studies, emphasizing the need for comprehensive study parameters and the importance of considering a wide range of effect sizes when evaluating the robustness and power of statistical tests. The article concludes that the revised effect size rules should be incorporated into the design of Monte Carlo studies to ensure more accurate and comprehensive results.