Standard error of mean is called as-
**Core Concept**
The standard error of the mean (SEM) is a statistical measure that estimates the variability of the mean of a sample from a larger population. It represents the standard deviation of the sampling distribution of the mean, indicating how much the sample mean is likely to deviate from the true population mean.
**Why the Correct Answer is Right**
The SEM is calculated as the standard deviation of the population divided by the square root of the sample size. This formula, SEM = σ / √n, where σ is the population standard deviation and n is the sample size, provides a measure of the precision of the sample mean. A smaller SEM indicates a more precise estimate of the population mean. The SEM is a crucial concept in statistical analysis, particularly in research studies where sample sizes are small.
**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because it does not accurately represent the standard error of the mean. The coefficient of variation (CV) is a measure of relative variability, calculated as the ratio of the standard deviation to the mean, but it is not the standard error of the mean.
**Option B:** This option is incorrect because the standard deviation of the mean is a related but distinct concept from the standard deviation of the population. While the standard deviation of the population provides a measure of the variability of individual data points, the standard error of the mean estimates the variability of the sample mean itself.
**Option C:** This option is incorrect because the standard error of the estimate is a measure of the variability of the regression coefficient, not the sample mean.
**Clinical Pearl / High-Yield Fact**
When interpreting the standard error of the mean, it's essential to consider the sample size and the magnitude of the effect being measured. A smaller SEM indicates a more precise estimate, but it's also important to evaluate the clinical significance of the findings.
**Correct Answer: C. Standard error of estimate.