During method validation, which term describes the closeness of the mean to the true value?

During method validation, which term describes the closeness of the mean to the true value?

• A. Trueness
• B. Precision
• C. Bias
• D. Robustness

Answer: (A)

The main idea here is trueness. Trueness describes how close the average of repeated measurements is to the true value, reflecting the method’s systematic error. In method validation, you assess trueness by measuring a known standard multiple times and looking at the mean of those results. If the mean matches the true value, trueness is high and bias is essentially zero. If the mean drifts away from the true value, that drift is bias, indicating a systematic error in the method. Precision, in contrast, is about how tightly the individual measurements cluster around that mean, i.e., the repeatability or reproducibility, not how close the mean is to the true value. Robustness is about the method’s performance when you slightly change conditions, not about closeness to the true value. For example, if the true value is 100 units and repeated measurements average to 100.0 with some small spread, trueness is high. If the average were 94, there would be bias of -6 units, showing poor trueness even if the individual results were still relatively consistent.

The main idea here is trueness. Trueness describes how close the average of repeated measurements is to the true value, reflecting the method’s systematic error. In method validation, you assess trueness by measuring a known standard multiple times and looking at the mean of those results. If the mean matches the true value, trueness is high and bias is essentially zero. If the mean drifts away from the true value, that drift is bias, indicating a systematic error in the method.

Precision, in contrast, is about how tightly the individual measurements cluster around that mean, i.e., the repeatability or reproducibility, not how close the mean is to the true value. Robustness is about the method’s performance when you slightly change conditions, not about closeness to the true value.

For example, if the true value is 100 units and repeated measurements average to 100.0 with some small spread, trueness is high. If the average were 94, there would be bias of -6 units, showing poor trueness even if the individual results were still relatively consistent.