An audit of ten quantization guides reveals a critical gap: none include threshold-dependent metrics like false-accept or false-reject rates. Despite widespread use of accuracy comparisons—such as INT8 accuracy ≥ FP32 accuracy − 1%—these gates fail to account for how quantization shifts score distributions. The article argues that without measuring threshold performance, even well-documented acceptance criteria can silently fail. Using a face verification case with a Hailo toolchain and Arc Face encoder, the author demonstrates that the failure is real and measurable. The fix requires just two additional metrics and modest effort, yet remains absent across industry documentation. This oversight isn't accidental—it reflects a structural blind spot in how quantization validation is designed.
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