We consider a variety of issues that arise when designing and analyzing computational experiments for global optimization. We describe a probability model for objective functions and a method for generating pseudorandom objective functions. We argue in favor of evaluating the performance of global optimization algorithms by measuring the depth of the objective function achieved with a fixed number of function evaluations. We emphasize the importance of replication in computational experiments and describe some useful statistical techniques for assimilating results. We illustrate our methods by performing a small study that compares two multistart strategies for global optimization.