The Replication Penalty in Resource-Constrained Predictive Modeling

Replication is a cornerstone of classical experimental design and is widely assumed to improve predictive modeling by reducing noise. We show analytically that this assumption fails under resource constraints, where replication is not only unnecessary but strictly detrimental to predictive accuracy. By deriving a simple closed-form solution for prediction error, we demonstrate that the optimal strategy is always to prioritize information diversity over measurement precision. This result provides the formal theoretical foundation for prior empirical observations, where maximizing unique observations consistently outperformed fewer observations with reduced noise via replication. Our findings offer a robust, broadly applicable framework for researchers engaged in predictive modeling who wish to maximize model performance by allocating their entire measurement budget to unique observations.