dysfunctional beliefs), which in turn influences the outcome variable (e.g., changes in depressive symptoms; [7]; see Fig. 1a). Such a mediation effect needs to be distinguished from a moderation effect, i.e., the effect of a third variable (e.g., age, gender, cognitive abilities) that influences the strength of the relationship between two variables (see Fig. 1b). While a moderation effect is able to identify for whom and under what circumstances a treatment produces its effects, a mediation effect helps to clarify how a treatment works [9]. Although methods to test mediation effects have grown in sophistication [10], the most ubiquitous method in the current literature is the ‘causal steps approach’ proposed by Baron and Kenny [11]. According to this approach, two models, a basic model and a mediation model, are used to evaluate mediation effects (see Fig. 1). The basic model postulates a significant association between the independent variable and the outcome variable (path c). The mediation model posits a significant association between the mediator and both the independent variable (path a) and the outcome variable (path b), while the direct relationship between the independent and the outcome variable (path c) should no longer be significant (i.e., complete mediation), or at least be substantially reduced (i.e. partial mediation), when the mediator is included in the model (path c′). The product of the path coefficients a and b quantifies the indirect mediated effect of the independent variable on the outcome [8]. However, this approach has been criticized for both its low power and the lack of quantification of the indirect, mediated effect, although the latter is most relevant [12]. Therefore, it is necessary to test the significance level of the indirect, mediated effect (i.e., product of coefficients a and b) and to estimate its confidence intervals [13]. Additionally, several options are available to calculate effect sizes for the indirect, mediated effect [14]. Consequently, the concept Introduction