What is described as the standard deviation of a distribution of means?

The standard deviation of a distribution of means is referred to as the standard error. This concept is crucial in statistics, particularly in the context of inferential statistics, where researchers often deal with sample means rather than population parameters.

The standard error quantifies the variability of sample means from the actual population mean. It is calculated by taking the standard deviation of the sample distribution and dividing it by the square root of the sample size. This measure provides insights into how much sampling error can be expected when estimating the population mean from a sample. As the sample size increases, the standard error decreases, indicating more precise estimates of the population parameter.

In contrast, variance refers to the average of the squared deviations from the mean within a dataset and does not directly relate to the distribution of means. Mean deviation is a similar but less common measure that reflects the average distance of each data point from the mean, while sample variance refers to the variance calculated from sample data rather than the entire population. Thus, the correct choice highlights the role of standard error in conveying reliability and precision in sampling.