The scenario addressed in this thesis is that of a swarm of agents (simulated robots) that needs to travel from an initial location to a goal location, while avoiding obstacles. Before deploying the entire swarm, it would be advantageous to have a certain level of confidence that a desired percentage of the swarm will be likely to succeed in getting to the goal. The approach taken in this thesis is to use a small group of expendable robot "scouts'' to predict the success probability for the swarm. Two approaches to solving this problem are presented and compared -- the standard Bernoulli trials formula, and a new Bayesian formula. Extensive experimental results are summarized that measure and compare the mean-squared error of the predictions with respect to ground truth, under a wide variety of circumstances. Experimental conclusions include the utility of a uniform prior for the Bayesian formula in knowledge-lean situations, and the accuracy and robustness to the changes in the environment of the Bayesian approach. The thesis also reports an intriguing result, namely, that both formulas usually predict better in the presence of inter-agent forces than when their independence assumptions hold.