Question

The measurement of sampling error is usually called:

• A. Significance level
• B. Confidence level
• C. Precision of sampling plan
• D. Validation of data

Hint:

Precision of sampling plan = how close your sample gets to the true population value, measured by standard error.

Answer 1

The Correct Option is C

Sampling error is the discrepancy between a sample statistic and the true population parameter, arising due to the use of a sample rather than the entire population. The {precision of a sampling plan measures how tightly sample estimates cluster around the true population value, typically quantified by the standard error or margin of error. A higher precision indicates a smaller sampling error. In contrast, the significance level (option 1) relates to hypothesis testing, the confidence level (option 2) describes the probability that a confidence interval contains the true parameter, and validation of data (option 4) refers to checking data accuracy, not sampling error. Thus, option (3) is correct.

Answer 2

Definitional elimination:
The significance level is a threshold used in hypothesis testing to decide when to reject a null hypothesis, it is not a direct measurement of sampling error. The confidence level expresses how often a confidence interval would contain the true parameter across repeated sampling, it describes reliability of the interval, not the size of the error itself. Validation of data concerns checking whether recorded data is accurate and consistent, unrelated to sampling error.
By contrast, the precision of a sampling plan is directly tied to the standard error of the estimate, roughly \( \text{Precision} \propto \frac{1}{\sqrt{n}} \), a smaller standard error means higher precision and a smaller sampling error. This is the only option that measures the error itself rather than a related but distinct statistical concept.
Hence, option (3) is correct.