A distributed hypothesis testing problem is considered, where the goal is to declare the distribution of two random variables, based on their observations. Defining two error events, the error exponent of Type II is studied under a fixed constraint over the error of type I. A novel approach is presented, based on random binning. The benefits of this approach are demonstrated through an example, compared to a more traditional approach, as well as to a different binned decoding method. These performance gains are then generalized to a large set of probability distributions.