Pseudorandom number generators are essential for various stochastic simulations in physics, economics, engineering, etc. In this study, we propose a practical method for estimating appropriate sample sizes for lagged Fibonacci generators, one of the most traditional and widely used pseudorandom number generators. This estimation is based on an analogy with the theoretical one-dimensional random walk. The proposed method is formulated using a weight enumeration polynomial and the MacWilliams identity in coding theory. Generally, a weight enumeration polynomial requires an intractable exhaustive check of the seeds. However, under certain conditions, the MacWilliams identity allows for the direct derivation of the weight enumeration polynomial. To improve efficiency, we employ a heuristic technique to approximate the weight enumeration polynomial by truncating “non-essential” terms motivated by Fourier analysis in signal processing.