Can the sample size of subgroups influence significance of correlations?

I am currently helping a friend with some statistical analysis.

There is a main hypothesis, for which a significant relationship can be shown. Lets call the predictor variable A and the predicted variable B. Her sample size is 200 people. However the distribution of the sexes is very uneven, i.e. there are 150 women and only 50 men.

There also is a significant relationship in her data between gender and the predicted variable B. Furthermore if all factors are analyzed together, the significance of the relationship between A and B drops below the alpha-level.

Is it possible that the unevenness of the sample can somehow influence the result in this case? Or is there some other effect that explains this situation. If so what is this effect called (assuming it is known). Maybe the stronger relationship between gender and B is shadowing the relationship between A and B or something like that.

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About your 3rd paragraph---"between sex and the predicted variable B and the sex"---do you mean there is some correlation between A and gender, and B and gender? –  chl Dec 3 '11 at 22:30
@chl: sorry, I guess I was missing some english words, when I typed this. I tried to edit to make this a little clearer. I am not sure about a correlation between variable A and gender. The interaction was tested using an ANOVA. The ANOVA of A and B was significant and the ANOVA between gender and B was significant. For an ANOVA with A, gender and B only the relationship between gender and B were significant, so the presence of gender in the analysis destroyed the significance for A and B. I hope it is more clear this way. –  LiKao Dec 3 '11 at 22:50