# Contradiction between significant effect in multiple regression, but non-significant t-test on its own

I ran a multiple regression using 10 independent variables and the single dependent variable (consumer complaining behaviour). One of those independent variables was gender. The $R^2$ for the model itself was $.157 (F= 20.50, p = .000)$ which whilst not the highest $R^2$ score was at least significant. Down in the coefficients table Gender $(\beta = -.083, p = .006)$. As my supervisor explained it is a significant score that accepts the alternative, and has a negative relationship with CCB. Interpretively, it would mean men are more likely to complain than women (men = 1 women = 2).

Now I got a bit curious and did a t-test to test the difference in means and as it turns out there is no significant difference between the gender groups.

This is where I'm getting a bit confused... I'm not sure how I'm meant to interpret these results. It just seems like maybe they contradict each other?