Do System Gains Reflect Useful Predictive Variation?

Scientific prediction systems can reduce error by correcting an overall offset, removing harmful baseline variation, or predicting useful differences between samples. How much of an observed gain comes from predicting sample differences? We compare fixed drug-response and histology predictions with controls that remove sample variation while retaining the reference adjustment. In four of 28 drug comparisons, the system improves on the adjusted baseline even though its added predictive variation increases error; removing the old variation accounts for the gain. In histology, source-fitted amplitude changes contribution without changing gene-wise correlation, and risk weighting changes the judgment of an identical output. Downstream Hallmark scoring introduces a further distinction. Across five models, 92 patients, and 50 gene sets, moving removal from expression to final scores reduces the number of positive simultaneous intervals from 39 to 14 out of 250 comparisons. The gap arises because the two operations produce constant controls with different means, changing the loss against which the same prediction is judged. These findings separate the value of a complete system improvement from the contribution of the sample differences it predicts.