In standard neuroevolution, the goal is to evolve a single neural network that is often able to compute a desired answer. The method of confidence attempts to extract even better answers from the entire population. One way to do this is do evolve networks that output not only their answer, but also an estimate of that answer's correctness. Experimental results in the handwritten digit recognition domain suggest that such an evolutionary process, combined with an effective technique for speciation, can create a population of networks that performs better than any individual network.