Sisvar is a widely used statistical analysis system in the scientific community for producing statistical analyses and scientific conclusions. It is known for its accuracy, precision, simplicity, and robustness. One of its lesser-known features is the use of bootstrap methods for multiple comparison procedures, which are particularly useful under non-normal, asymmetric, platykurtic, or leptokurtic distributions. This paper reviews the application of Sisvar's bootstrap multiple comparison procedures, highlighting their advantages over traditional methods like Dunnett, Tukey, Student-Newman-Keuls, and Scott-Knott tests. The review demonstrates that the bootstrap methods provide greater power and better control of experiment-wise type I error rates. The paper also includes Monte Carlo simulations to evaluate the performance of these methods, showing that the bootstrap tests control the error rates more effectively, especially under non-normal conditions. The conclusion recommends the use of Sisvar's bootstrap multiple comparison procedures for their superior performance in handling non-normal data.Sisvar is a widely used statistical analysis system in the scientific community for producing statistical analyses and scientific conclusions. It is known for its accuracy, precision, simplicity, and robustness. One of its lesser-known features is the use of bootstrap methods for multiple comparison procedures, which are particularly useful under non-normal, asymmetric, platykurtic, or leptokurtic distributions. This paper reviews the application of Sisvar's bootstrap multiple comparison procedures, highlighting their advantages over traditional methods like Dunnett, Tukey, Student-Newman-Keuls, and Scott-Knott tests. The review demonstrates that the bootstrap methods provide greater power and better control of experiment-wise type I error rates. The paper also includes Monte Carlo simulations to evaluate the performance of these methods, showing that the bootstrap tests control the error rates more effectively, especially under non-normal conditions. The conclusion recommends the use of Sisvar's bootstrap multiple comparison procedures for their superior performance in handling non-normal data.