Identifying mediators in variable chains as part of a causal mediation analysis can shed light on issues of causation, assessment, and intervention. However, coefficients and effect sizes in a causal mediation analysis are nearly always small. This can lead those less familiar with the approach to reject the results of causal mediation analysis. The current paper highlights five factors that contribute to small path coefficients in mediation research: loss of information when measuring relationships across time, controlling for prior levels of a predicted variable, adding control variables to the analysis, ignoring measurement error in one's variables, and using multiple mediators. It is argued that these issues are best handled by increasing the statistical power of the analysis, identifying the optimal temporal interval between variables, using bootstrapped confidence intervals to analyze the results, and finding alternate ways of assessing the meaningfulness of the indirect effect.