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I collect $n$ ($<20$) i.i.d. observations from any distribution. In order to compute the sample variance, I take $$s^2=\sum_i \frac{(\bar{X}-X_i)^2}{n-1}$$

If I want to build a confidence interval for the mean, I need the standard error of the sample mean. Do I divide by $n$, or by $n-1$ again?

$$S.E. \stackrel{?}{=}\sqrt{\frac{s^2}{n-1}}$$

Glen_b
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Wuschelbeutel Kartoffelhuhn
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  • The first part of @Macro's answer [here](http://stats.stackexchange.com/a/29644/805) does the necessary derivation, and I think it's covered by the answers [here](http://stats.stackexchange.com/questions/33547/how-does-the-standard-error-work). – Glen_b Jun 05 '13 at 08:28

1 Answers1

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The sample mean is given by:

$$\bar{x}=\frac{\sum x_i}{n}$$

Thus, the standard error of the sample mean is:

$$SE = \sqrt{V(\bar{x})}$$

But:

$$V(\bar{x})=V(\frac{\sum x_i}{n})=\frac{\sum V(x_i)}{n^2}=\frac{nS^2}{n^2}=\frac{S^2}{n}$$

Therefore, you divide by $n$ and not by $n-1$.