Standard Deviation of Sample Mean Calculator
Compute the standard deviation of the sampling distribution of the mean from population standard deviation and sample size.
What the Standard Deviation of the Sample Mean Measures
When you draw many samples of size $n$ from a population, the sample means form a sampling distribution. Its spread is the standard deviation of the sample mean, also called the standard error when population $\sigma$ is known:
$$\sigma_{\bar{X}} = \frac{\sigma}{\sqrt{n}}$$
Smaller values mean sample means cluster closer to $\mu$. Related tools: Central Limit Theorem Calculator and Standard Error Calculator.
Example
If $\sigma = 7.1$ and $n = 100$, then $\sigma_{\bar{X}} = 7.1 / \sqrt{100} = 0.71$. Sample means vary much less than individual observations.
Frequently Asked Questions
Is this the same as standard error?
When population standard deviation is known, $\sigma_{\bar{X}}$ equals the standard error of the mean. If you only know sample $s$, use $s/\sqrt{n}$ instead.
Why divide by the square root of n?
Averaging $n$ observations reduces variability. The reduction factor is $\sqrt{n}$, not $n$ itself.
Does the mean of the sampling distribution equal the population mean?
Yes. The mean of sample means equals $\mu$, while the standard deviation of sample means equals $\sigma/\sqrt{n}$.
What happens when sample size increases?
The standard deviation of the sample mean decreases. Larger samples produce more precise estimates of the population mean.