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Standard Deviation of Sample Mean Calculator

Compute the standard deviation of the sampling distribution of the mean from population standard deviation and sample size.

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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.