Repeated measures ANOVA with significant interaction effect, but non-significant main effect I am running a two-way repeated measures ANOVA (main effects: Time, Condition). The result is that the main effect of time is significant (P<0.05), the main effect of condition is not significant (P>0.05), and the interaction effect (time*condition) is significant (P<0.05).
Considering there is a significant interaction effect, we have ran Tukey post hoc testing to decompose the data points at each time and determine if differences exist. Although to my understanding this is acceptable, our approach has recently been questioned as an individual has suggested you need all main effects to be significant prior to further investigation into the significant interaction effect.
Does anyone have any thoughts/articles that may support/refute my approach. Kind regards,

Inadvertently, you're comparing apples and oranges. Once an interaction is included in the model, the interpretation of the main effect changes. With the interaction, the main effect of "condition" is the expected difference at time 0, and the effect of time is the slope of time trend for the referent condition.
The most likely scenario explaining your finding here is that the interaction projects the difference and slope of these effects to 0 at the specific timepoint. It of course does not mean you should remove them from the model - the interaction effect will no longer be interpretable. In the example plot below, I show two trendlines for a continuous variable X for a binary factor W. When the interaction is included in the model, the X trend is significant but the W effect is no longer significant. That's because when X=0, the values of Y are exactly the same whether W=0 or 1. However, it's clear from the graphic that both W and X are "significant" variables in the sense that the response is strongly predicted by their inclusion in the model - specifically the interaction.

