This review discusses mediation analysis in psychology, focusing on the role of mediating variables in explaining how one variable affects another. Mediating variables transmit the effect of an independent variable on a dependent variable, and differ from confounders, moderators, and covariates. The review outlines statistical methods to assess mediation, including the single-mediator model, and discusses future directions for mediation analysis. It highlights the importance of mediating variables in psychological theories and research, and provides examples of their application in prevention and treatment research. The review also addresses methodological challenges in assessing mediation, such as the complexity of three-variable systems and the need for accurate statistical testing. It discusses the use of mediation analysis in experimental studies, including randomized designs, and the importance of identifying and testing mediating variables in treatment and prevention research. The review also covers various statistical methods for assessing mediation, including the causal steps approach, product of coefficients method, and difference in coefficients method. It discusses the limitations of these methods, the importance of effect size measures, and the need for further research on mediation models. The review also addresses the extension of the single-mediator model to multiple mediators, longitudinal mediation models, and models with moderators. It discusses the importance of considering temporal precedence in mediation models and the use of longitudinal data to examine mediation effects. The review also addresses the distinction between moderation and mediation, and the use of moderated mediation models to examine how the effect of an independent variable on a dependent variable may depend on the level of a moderator. The review concludes with a discussion of causal inference in mediation analysis and the need for further research to develop and test general models that incorporate both mediation and moderation effects.This review discusses mediation analysis in psychology, focusing on the role of mediating variables in explaining how one variable affects another. Mediating variables transmit the effect of an independent variable on a dependent variable, and differ from confounders, moderators, and covariates. The review outlines statistical methods to assess mediation, including the single-mediator model, and discusses future directions for mediation analysis. It highlights the importance of mediating variables in psychological theories and research, and provides examples of their application in prevention and treatment research. The review also addresses methodological challenges in assessing mediation, such as the complexity of three-variable systems and the need for accurate statistical testing. It discusses the use of mediation analysis in experimental studies, including randomized designs, and the importance of identifying and testing mediating variables in treatment and prevention research. The review also covers various statistical methods for assessing mediation, including the causal steps approach, product of coefficients method, and difference in coefficients method. It discusses the limitations of these methods, the importance of effect size measures, and the need for further research on mediation models. The review also addresses the extension of the single-mediator model to multiple mediators, longitudinal mediation models, and models with moderators. It discusses the importance of considering temporal precedence in mediation models and the use of longitudinal data to examine mediation effects. The review also addresses the distinction between moderation and mediation, and the use of moderated mediation models to examine how the effect of an independent variable on a dependent variable may depend on the level of a moderator. The review concludes with a discussion of causal inference in mediation analysis and the need for further research to develop and test general models that incorporate both mediation and moderation effects.