Which of the following is an example of non-sampling error?

• A. Biased survey question wording leading to biased responses.
• B. Increasing sample size reduces sampling error.
• C. Using convenience sampling.
• D. Nonresponse bias is unrelated to data quality.

Answer: (A)

Non-sampling error covers mistakes that creep into data collection, measurement, or processing rather than random variation from choosing a sample. Biased survey question wording is a classic example because the error comes from how the questions are asked, which can systematically push responses in a particular direction. This type of bias affects data quality independent of how large the sample is or how carefully you sample.

In contrast, increasing sample size reduces sampling error—the random fluctuation you get when estimating a population from a subset, not a measurement or response bias. Using convenience sampling is primarily about who is included in the sample; it creates bias through the sampling method itself, which is a sampling design issue and can affect representativeness rather than a measurement or processing error. Nonresponse bias is another non-sampling problem, and it actually relates to data quality because it arises when those who don’t respond differ in important ways from responders.

So the wording bias is the best illustration of non-sampling error here.