Unrecognized systematic errors can survive standard checks and undermine scientific discoveries. Null tests and experimental controls can expose these failures, but averaging measurements or reducing them to scalar statistics can hide important structure. We propose ensemble null tests that compare multivariate distributions with references from observations or simulations. We demonstrate their benefits across science. In cosmic microwave background simulations, they detect systematic effects missed by conventional $χ^2$ tests. In Gaia data, they detect velocity asymmetry as sensitively as an informed search. In a CRISPR screen, they expose a control mismatch in DNA damage and reveal which cellular profiles drive it. As scientific datasets grow, ensemble testing turns repeated measurements into a broader search for unknown unknowns, strengthening the empirical foundation for scientific discovery.

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