This paper discusses the complexities of computing interaction effects and standard errors in nonlinear models, particularly logit and probit models. The authors explain that the marginal effect of a change in two variables in nonlinear models is not straightforward and can differ from linear models. They introduce the `inteff` command in Stata, which correctly calculates the marginal effect and standard errors of interaction terms in logit and probit models. The command also graphs the interaction effect and saves the results for further analysis. The paper provides detailed formulas for computing the interaction effect in nonlinear models and highlights the limitations of using linear model intuition in nonlinear contexts. Examples using real data illustrate how the `inteff` command can be applied to understand the interaction effects in practical scenarios. The authors emphasize the importance of the `inteff` command for applied researchers to accurately interpret interaction effects in nonlinear models.This paper discusses the complexities of computing interaction effects and standard errors in nonlinear models, particularly logit and probit models. The authors explain that the marginal effect of a change in two variables in nonlinear models is not straightforward and can differ from linear models. They introduce the `inteff` command in Stata, which correctly calculates the marginal effect and standard errors of interaction terms in logit and probit models. The command also graphs the interaction effect and saves the results for further analysis. The paper provides detailed formulas for computing the interaction effect in nonlinear models and highlights the limitations of using linear model intuition in nonlinear contexts. Examples using real data illustrate how the `inteff` command can be applied to understand the interaction effects in practical scenarios. The authors emphasize the importance of the `inteff` command for applied researchers to accurately interpret interaction effects in nonlinear models.