This paper proposes a dichotomous choice model that is based on a transformed beta (or "z") distribution. This model, called betit, nests both logit and probit and allows for various skewed and peaked disturbance densities. Because the shape of this density affects the estimated relation between the dichotomous choice variable and its determinants, the greater flexibility of the transformed beta distribution is useful in generating more accurate representations of this relationship. The paper considers asymptotic biases of the logit and probit models under conditions where betit should have been used. It also investigates small sample power and provides two examples of applications that illustrative of the capability of the betit model.