If repeated measurements are close to each other but far from the standard, the result is:

If repeated measurements are close to each other but far from the standard, the result is:

• A. Not accurate, precise
• B. Accurate, precise
• C. Accurate, not precise
• D. Not accurate, not precise

Answer: (A)

The main idea is distinguishing precision from accuracy. Precision looks at how close repeated measurements are to each other, while accuracy checks how close those measurements are to the true value (the standard). Here, the measurements cluster tightly, so they’re precise. But that cluster is far from the standard, meaning the measurements miss the true value consistently—low accuracy. So the result is precise but not accurate. If something were both accurate and precise, the tight cluster would sit right near the standard. If it were accurate but not precise, the measurements would be spread out but centered around the standard. If neither accurate nor precise, the spread would be wide and the values would be far from the standard. To improve accuracy, you’d look for and fix systematic bias; to improve precision, you’d reduce random errors.

The main idea is distinguishing precision from accuracy. Precision looks at how close repeated measurements are to each other, while accuracy checks how close those measurements are to the true value (the standard).

Here, the measurements cluster tightly, so they’re precise. But that cluster is far from the standard, meaning the measurements miss the true value consistently—low accuracy. So the result is precise but not accurate.

If something were both accurate and precise, the tight cluster would sit right near the standard. If it were accurate but not precise, the measurements would be spread out but centered around the standard. If neither accurate nor precise, the spread would be wide and the values would be far from the standard. To improve accuracy, you’d look for and fix systematic bias; to improve precision, you’d reduce random errors.