What is described as the discrepancy between a sample statistic and the corresponding population parameter?

The term that describes the discrepancy between a sample statistic and the corresponding population parameter is known as sampling error. This occurs because a sample is only a subset of the entire population, and there will naturally be variations between the characteristics of the sample and those of the entire population. Sampling error is a fundamental concept in statistics, highlighting that while samples are used to estimate parameters, they can never perfectly reflect the population due to inherent differences, sampling methods, and size.

In contrast, non-sampling error refers to other types of errors that can occur in data collection or analysis, often unrelated to the sampling process itself, such as measurement errors or biases in survey responses. Cyclical effects pertain to trends or patterns that fluctuate over time, rather than focusing on discrepancies between samples and populations. Probabilistic sampling is a method of sampling where each member of the population has a known and non-zero chance of selection, which aids in reducing sampling error but does not describe the error itself.

Overall, sampling error is crucial for understanding the limits of statistical inferences and is essential for making accurate predictions and decisions based on sample data.