This is the difference between a sample statistic and the corresponding population parameter.

This is the difference between a sample statistic and the corresponding population parameter. Options: (a) Standard error (b) Sampling error (c) Difference error (d) None ✅ Correct Option: (b) Sampling error Explanation (200+ words): Sampling error refers to the difference between a sample statistic (such as sample mean) and the corresponding population parameter (such as population mean). This difference arises naturally because samples rarely represent populations perfectly. Sampling error is unavoidable but can be reduced by increasing sample size or using proper sampling techniques. It is not a mistake but a natural consequence of using samples instead of entire populations. Standard error, on the other hand, measures the variability of a statistic across different samples, not the difference between a sample statistic and a population parameter. Therefore, option (b) is correct. 10 PPSC-Related Facts: 1. Sampling error ≠ mistake 2. Occurs due to sampling 3. Reduced by large samples 4. Difference between statistic & parameter 5. Natural phenomenon 6. Important inferential concept 7. Related to estimation 8. Not avoidable completely 9. Common exam MCQ 10. Basis of standard error