This research investigates the implications of lossy verification in Speculative Decoding (SD), a technique designed to accelerate large language model inference by utilizing a lightweight draft model to propose tokens for parallel verification by a target model. While relaxing strict distributional matching can enhance efficiency, the authors demonstrate that this often silently distorts the decoding distribution, leading to unstable or degraded generation quality. Through a principled analysis, the study unifies existing lossy verification approaches into two primary categories: truncation-based verification and collaborative verification. For truncation-based methods, the researchers identify a significant pitfall where performance drops below the true truncation sampling baseline due to distributional distortion. In the case of collaborative verification, the findings suggest that effectiveness is driven more by overshoot suppression and supervision quality than by simple linear interpolation between draft and target distributions. To support these findings, the authors developed a diagnostic evaluation framework across curated benchmarks to systematically measure these trade-offs. The work provides a theoretical foundation for understanding how relaxing verification constraints impacts the balance between computational acceleration and linguistic accuracy in speculative decoding frameworks.