A new paper from Apple Machine Learning Research proves that location-invariant properties of functions and distributions diverge during verification. While these properties share similar complexities during testing, the relationship fails when verifying specific instances. This distinction limits how researchers apply distribution sampling techniques to function verification. The result clarifies theoretical bounds for algorithmic verification.