%0 Journal Article %T Visual heuristics for marginal effects plots %A Thomas B. Pepinsky %J Research & Politics %@ 2053-1680 %D 2018 %R 10.1177/2053168018756668 %X Common visual heuristics used to interpret marginal effects plots are susceptible to Type-1 error. This susceptibility varies as a function of (a) sample size, (b) stochastic error in the true data generating process, and (c) the relative size of the main effects of the causal variable versus the moderator. I discuss simple alternatives to these standard visual heuristics that may improve inference and do not depend on regression parameters %K Interaction terms %K marginal effects plots %K conditional hypotheses %K data visualization %U https://journals.sagepub.com/doi/full/10.1177/2053168018756668