American Board of Surgery Qualifying Exam (ABS QE) Practice Test

Which of the following best describes standard error?

1. A measure of the spread of nominal data

2. A measure that depends on sample size

3. A fixed measurement of variability

4. The difference between the highest and lowest scores

The correct answer is: A measure that depends on sample size

The standard error is a statistical term that quantifies the amount of variability or dispersion in a sampling distribution of a statistic, typically the mean. It specifically measures how far the sample mean is likely to be from the population mean. One of the key characteristics of standard error is that it is inversely related to the sample size. As the sample size increases, the standard error decreases, reflecting that larger samples tend to produce more reliable estimates of the population parameter. This dependence on sample size is crucial because it highlights how the precision of the sample mean as an estimate of the population mean improves with larger samples. Other options describe different concepts that do not accurately depict standard error. For instance, while spread can refer to variability in data, it does not relate specifically to how the standard error functions. A fixed measurement of variability does not align with standard error since standard error varies depending on the size of the sample from which it is calculated. Similarly, the difference between the highest and lowest scores describes the range of a data set, which is a separate statistical measure and does not pertain to standard error.