In verification terms, what is the difference between accuracy and precision?

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Accuracy tells you how close a measurement is to the true value, while precision tells you how repeatable or reproducible measurements are under the same conditions.

Think of measuring a known standard. If the measurements cluster tightly around the true value, you’re both accurate (the average is near the true value) and precise (little spread). If the measurements are all close to the true value on average but vary a lot, you’re accurate on average but not precise. If the results are consistently close to one value but away from the true value, they’re precise but not accurate. These ideas capture the essential difference between accuracy (bias toward the true value) and precision (consistency of results).

The other options reference speed, data processing, or a general risk of misinterpretation, but they don’t describe what accuracy and precision mean in verification—the clear distinction is closeness to the true value versus reproducibility.