Interpretation of significant interaction term, but insignificant joint effect I have a very basic interaction model with this setup:
Y = $\beta_0$ + $\beta_1$ Var_1 + $\beta_2$ Var_2 + $\beta_3$ Var_1 * Var_2 + Controls + $\epsilon$

*

*Y = Continuous variable

*Var_1 = Continuous variable

*Var_2 = Indicator variable (i.e., 1 or 0)

In this model, $\beta_3$ is significant--but joint test of $\beta_1$ + $\beta_3$ (i.e., when Var_2 = 1) is insignificant.
How do I interpret the interaction term when the total effect isn't significant?
Edit:
My post has been identified as a potential duplicate, but I don't think that this is the case because those links appear to be concerned with the interpretation of main effects when an interaction effect makes the main effects insignificant.
I'm interested in the interpretation of a significant interaction effect when the total effect (i.e., $\beta_1$ + $\beta_3$) is insignificant.
 A: It's worth considering what an interaction is.  The existence of an interaction means that the relationship between one variable and the response varies as a function of the other variable.  It doesn't mean that the relationship differs from 0 at any particular level (although it must somewhere).  Consider the simplest case: a balanced design with two categorical variables with two levels each.  Let's say that the data within each combination of levels is normally distributed and with the same variance.  Let's further stipulate that the means are as listed below, and the standard errors on the slopes are 0.3:  
        Var 2
Var 1   A     B
    A   0    -0.5
    B  -0.5   0

Now, in a model where level A is taken as the reference level, the interaction would be significant, but neither main effect would be.  Moreover, the linear contrast of Var 1 + interaction would mostly cancel itself out, because the effects move in opposite directions and the standard error of the sum of the two slopes would increase.  
A: It just says that effect of Var_1 on Y vanish (disappear) when Var_2=1. Indeed because b1 is significant it means that when Var_2=0 then marginal effect of Var_1 on Y is b1. However when Var_2=1 then marginal effect is b1+b3 that is statistically insignificant differ from zero (as you have tested).
