Interpretation of significant interaction term, but insignificant joint effect [duplicate]

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.

• Can you explain a bit further what your scientific hypothesis is? Dec 30, 2016 at 12:47
• Why would significance (or lack thereof) of an estimate have any effect whatsoever on its interpretation? Are you asking about what the coefficients mean in the model or how to react to the associated p-values?
– whuber
Dec 30, 2016 at 15:49
• This doesn't seem like quite a duplicate to me. Dec 30, 2016 at 19:18
• @mdewey: I think that this helps answer my question. I need to think about what each comparison means. B1 + B3 indicates the total effect of the condition where Var2 = 1 on Y. B3 tells me that there's an incremental difference between B1 and Y in the presence of Var2.
– Tots
Dec 30, 2016 at 19:35
• @whuber: I was wanting to know how to interpret each coefficient in the presence of each of the associated p-values. I think that I have it now. The interaction says that there's a significant difference in the relation between Var1 and Y in the presence of Var2, but then the total effect indicates that the total effect isn't different from zero (i.e., B1 + B3 is insignificant).
– Tots
Dec 30, 2016 at 19:37

        Var 2

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.