Which term describes the discrepancy between a sample and the true population?

Which term describes the discrepancy between a sample and the true population?

• A. Confidence interval
• B. Margin of error
• C. Sample variance
• D. Standard deviation

Answer: (B)

The correct choice describes the margin of error as the term that represents the discrepancy between a sample and the true population. This term specifically pertains to the range within which we can expect the true population parameter to lie, based on the sample statistics and the level of confidence chosen. The margin of error quantifies the uncertainty of estimates derived from a sample. For example, if a poll indicates that 60% of voters support a candidate, the margin of error might be ±3%. This means that the true level of support in the overall population could realistically be between 57% and 63%. It essentially provides a buffer to account for the natural variability that occurs when working with samples instead of the whole population. In contrast, confidence intervals provide a similar concept but represent the entire range of values around a sample statistic, based on a certain confidence level. Sample variance and standard deviation, while related to how data points in a sample disperse around the mean, do not specifically address the discrepancy between a sample estimate and the true population value.

The correct choice describes the margin of error as the term that represents the discrepancy between a sample and the true population. This term specifically pertains to the range within which we can expect the true population parameter to lie, based on the sample statistics and the level of confidence chosen.

The margin of error quantifies the uncertainty of estimates derived from a sample. For example, if a poll indicates that 60% of voters support a candidate, the margin of error might be ±3%. This means that the true level of support in the overall population could realistically be between 57% and 63%. It essentially provides a buffer to account for the natural variability that occurs when working with samples instead of the whole population.

In contrast, confidence intervals provide a similar concept but represent the entire range of values around a sample statistic, based on a certain confidence level. Sample variance and standard deviation, while related to how data points in a sample disperse around the mean, do not specifically address the discrepancy between a sample estimate and the true population value.