When configuring a neural network, identifying the single best parameter choice from a massive combination space (e.g., $2^{17} = 131072$ combinations) is computationally infeasible and offers little insight for future architectures. Furthermore, this approach is flawed because due to the stochasticity in training (e.g., rounding, shuffling, bit errors), no two runs are likely to produce exactly the same results. Instead, an effective strategy relies on the assumption that general design principles exist and many networks will perform well. By sampling uniformly from the space of configurations and evaluating their performance, researchers can analyze the distribution of accuracies to discover simple rules and general guidelines that relate parameter choices.