This paper by Hothorn, Bretz, and Westfall discusses simultaneous inference in general parametric models, addressing the issue of multiple testing where the probability of incorrectly rejecting at least one null hypothesis increases beyond the pre-specified significance level. The authors extend the canonical theory of multiple comparison procedures in ANOVA models to a broader class of models, including linear regression, generalized linear models, linear mixed effects models, survival models, and robust linear models. They provide a unified framework that allows for arbitrary linear functions of the elemental parameters and feasible computation of the reference distribution for various designs, especially unbalanced ones. The paper includes theoretical results and practical examples using the R add-on package `multcomp`, which facilitates the implementation of these procedures. Key applications are illustrated through examples in multiple linear regression, one-way ANOVA, generalized linear models, and survival analysis, demonstrating the versatility and effectiveness of the proposed methods.This paper by Hothorn, Bretz, and Westfall discusses simultaneous inference in general parametric models, addressing the issue of multiple testing where the probability of incorrectly rejecting at least one null hypothesis increases beyond the pre-specified significance level. The authors extend the canonical theory of multiple comparison procedures in ANOVA models to a broader class of models, including linear regression, generalized linear models, linear mixed effects models, survival models, and robust linear models. They provide a unified framework that allows for arbitrary linear functions of the elemental parameters and feasible computation of the reference distribution for various designs, especially unbalanced ones. The paper includes theoretical results and practical examples using the R add-on package `multcomp`, which facilitates the implementation of these procedures. Key applications are illustrated through examples in multiple linear regression, one-way ANOVA, generalized linear models, and survival analysis, demonstrating the versatility and effectiveness of the proposed methods.