Which of the following definitions best describes ‘sampling error’?

Sampling error is defined as the difference between a sample statistic (such as a sample mean or proportion) and the corresponding population parameter (such as the true population mean or proportion). This discrepancy arises because a sample, which is a subset of the population, may not perfectly represent the entire population. Factors influencing this difference include the size of the sample and the randomness of the sample selection process.

In statistics, recognizing sampling error is crucial because it affects the reliability and accuracy of inferences made about the population based on sample data. Unlike biases or data entry errors, which may stem from human factors or methodological flaws, sampling error is a natural part of the sampling process and influences statistical inference, confidence intervals, and hypothesis testing. Thus, understanding and quantifying sampling error is essential for proper data analysis and interpretation.