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Equivariant graph neural networks have become central to data-efficient modeling of interatomic potentials, yet the mechanism underlying their empirical advantage remains insufficiently quantified. This work establishes a theoretical link between E(3)/SE(3) equivariance and sample complexity reduction in materials GNNs. By constraining the hypothesis space to functions that respect physical symmetries, equivariant architectures eliminate orbit-related redundancy and reduce the effective dimension of the learning problem. Within a PAC-style framework, we derive a bound showing that the sample complexity of an equivariant hypothesis class is reduced relative to its unrestricted counterpart by a factor proportional to the symmetry group size |G|, up to logarithmic terms. For discrete crystallographic groups, this yields finite reductions (e.g., 48× for cubic symmetry), while for continuous groups such as SO(3), the reduction becomes unbounded in the finite-data regime. The analysis relies on group-averaging projections and the induced contraction of VC dimension. These results provide a quantitative explanation for the observed data efficiency of architectures such as NequIP, MACE, and Allegro, and clarify the limitations of non-equivariant baselines such as SchNet. The framework situates symmetry as a statistically optimal inductive bias and offers principled guidance for designing data-efficient models in materials discovery.