Computational stability refers to the behavior of numerical algorithms when small changes in input lead to small changes in output. In other words, a stable algorithm produces consistent results even when faced with slight variations or errors in data. This is crucial in fields like computer science and engineering, where precise calculations are necessary. In contrast, an unstable algorithm can amplify errors, resulting in significant discrepancies in outcomes. Stability is often assessed through techniques like error analysis, which helps identify how errors propagate through computations. Ensuring computational stability is essential for reliable and accurate results in simulations and modeling.