Accuracy vs. precision — correct interpretation: When discussing measurement quality, which statement correctly characterizes the terms “accuracy” and “precision”?

Introduction / Context:
Clear terminology is vital in labs and field testing. Accuracy and precision are complementary but distinct: accuracy refers to closeness to the true value; precision refers to the spread of repeated measurements. Confusing them leads to poor decisions about calibration, averaging, and instrument selection.

Given Data / Assumptions:

• Measurements are repeated under similar conditions.
• True value (reference) is conceptually known for explanation.
• Random and systematic errors may exist simultaneously.

Concept / Approach:

Precision is about repeatability—low variability among replicates. Accuracy is about trueness—low bias from the reference. A process can be precise but inaccurate (systematically off) or accurate but imprecise (scattered around truth). The statistically minded map precision to standard deviation and accuracy to bias of the mean.

Step-by-Step Solution:

Verification / Alternative check:

Plot two datasets: Dataset A tightly clustered but offset from the true value (high precision, low accuracy). Dataset B widely scattered around the true value (low precision, moderate accuracy). The visuals make the distinction intuitive.

Why Other Options Are Wrong:

• Options A and B rank one as “less error,” which conflates bias and variance.
• Option D claims equivalence; they are distinct concepts.
• Option E reverses roles; variability around the mean is a precision concept, not accuracy.

Common Pitfalls:

• Averaging many imprecise measurements may improve accuracy of the mean but does not increase single-shot precision.
• Overlooking calibration drift that hurts accuracy even if precision remains high.

Final Answer:

“precision” measures the repeatability of a measurement