What is the difference between single-measurement verification and repeated measures verification?

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The main idea tested is whether a verification uses a single data point or multiple data points to judge performance. With a single-measurement verification, you take one value per check and decide if that single reading falls within the accepted tolerance. With repeated measures verification, you take several readings of the same standard to see how much the measurements vary, which reveals the precision and consistency of the instrument or process. In practice, you’d look at the average of those readings and the spread (like the range or standard deviation) to assess both accuracy and precision over time. This distinction matters because a single reading can pass by chance even if there’s underlying variability, whereas multiple readings expose that variability and help ensure the measurement system is reliable. The other options either swap the concepts, claim they’re the same, or imply automatic outlier removal, which isn’t how repeated measures work.