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testing sex differences: interaction vs splitting the sample by sex

Hi all,

I'm having a dilemma with the choice of statistical tests for my PhD, and wondered if you could give your opinion. 

I'm studying the dosage effect of a particular gene on social behaviour in mice and I'd like to know whether there is an effect in both (or either) males and females, as this gene has an effect on endocrine function in males but not females. I've run whole group analysis (2-way ANOVA (genotype x sex)) and after that have split the sample into males and female and run separate one-way ANOVAS, looking just at the effect of genotype.  

I do not see a significant two-way interaction or a significant main effect of sex in the whole group analysis. However, after running separate tests on the sexes, I see an effect in males, but not in females. 

I understand that the correct way of testing would usually be to do an interaction analysis, and only split by group if the interaction term is significant. In this case there is no significant interaction term. Therefore if I choose this option, I have no effect in the whole group and no interaction effect. However, if I choose such an approach, I "miss out" on the effect that we see in males.

I'm advocating to treat males and females as two separate populations given that there is already evidence to suggest that this particular gene effects males and females separately. Consequently, I would just run separate ANOVAs on the sexes, and would not compare males vs females when interpreting the results. 

However my supervisor feels that the best way to address this would be to run 3 separate one-way ANOVAs firstly on the whole sample, and then on males and females separately, but altering the accepted alpha level to .016 to account for the multiple corrections. However i'm not sure if this would be conventionally statistically acceptable, and i'm worried about being pulled up on this in my viva. 

I'd really appreciate any thoughts about either of the statistical options I'm considering, and more than happy to clarify anything that doesn't make sense!
gpt
If the interaction term isn't significant then it means that the slopes aren't significantly different, at least with one of your IVs anyway.

You can still report the post hoc comparisons as exploratory analyses but you should definitely still report this factorial ANOVA.