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Round-off error arises when numbers or arithmetic results are rounded to a finite set of representable values. Its scale is associated with machine epsilon, and its effects depend on subsequent operations. Even a formula with zero truncation error can lose information. In decimal arithmetic rounded to three significant digits, $1.234$ becomes $1.23$ and $1.233$ becomes $1.23$: subtracting the stored numbers gives zero although the exact difference is $0.001$. The subtraction exposes errors already present in its nearly equal operands.

This example also has a large condition number: independently perturbing the two inputs of $a-b$ can produce a relative amplification $(|a|+|b|)/|a-b|=2467$. High sensitivity belongs to the input-output problem; numerical stability concerns the arithmetic used to solve it. Conversely, on a common binary system with 53 significant bits and rounding to nearest, $(10^{16}+1)-10^{16}$ evaluates to zero while $(10^{16}-10^{16})+1$ evaluates to one. Exact arithmetic makes both one: changing summation order changes the accumulated roundoff error. MIT's summation lecture explains why numerical stability and the condition number must be assessed separately.^[ref]