Sampling error sample fluctuation – UGC NET Question

Sampling error refers to the natural discrepancy between sample statistics and population parameters that arises because only part of the population is observed. Even with perfectly random sampling and accurate measurement, different samples will yield slightly different estimates. This variability is quantified in terms such as standard error and confidence intervals. Hence, the difference described in the stem is correctly called sampling error.

Option A:
Measurement error occurs when the instruments or procedures used do not yield perfectly accurate values, leading to inaccuracies that are distinct from the randomness of sampling. It is related to how variables are measured rather than to the fact that only a subset is observed. Therefore, measurement error is not the best completion.

Option B:
Systematic error is a consistent bias in measurement or procedure that pushes results in one direction, such as miscalibration or interviewer bias. It does not average out across samples and is not due simply to using a subset. Consequently, systematic error is not what the stem is defining.

Option C:
Sampling error arises even in well designed probability samples and can be estimated and controlled by appropriate sample sizes. It reflects sample-to-sample fluctuation, making it the correct term for the discrepancy between sample and population values mentioned in the question.

Option D:
Non-response error occurs when individuals who fail to respond differ systematically from those who do, introducing bias, but this is a specific non-sampling error, not the inherent fluctuation due to partial observation. Thus, non-response error is not the accurate completion.