What should be done with a scientific model whose predictions are not confirmed experimentally?

• A. Revise and eventually abandon
• B. Keep as is
• C. Immediately abandon
• D. Ignore discrepancies

Answer: (A)

When a scientific model makes predictions, those predictions must be tested against real observations. If experiments don’t confirm what the model predicts, the right move is to revise the model to address the mismatch—adjust assumptions, add missing mechanisms, or refine parameters. If, after thorough testing, the revised version still can’t account for the data, the model should be abandoned or replaced with a more accurate framework. This reflects science’s self-correcting nature: it uses evidence to improve explanations rather than clinging to ideas that don’t fit. Discrepancies shouldn’t be ignored or kept as-is, though sometimes a mismatch could point to experimental error or limited applicability, which is why revision rather than immediate discard is the appropriate path.