# Calculating uncertainty in standard deviation

I have a distribution with literally an infinite number of potential data points. I need the standard deviation. I generate about a hundred points and take the standard deviation of the points. This gives a hopefully good approximation of the true standard deviation, but it won't, of course, be exact. How do I estimate the uncertainty in the standard deviation? This seems like a very basic question, but web searching hasn't provided any solution. If I missed it somehow, my apologies.

• What do you mean by uncertainty in the standard deviation? – velut luna Apr 30 '15 at 16:44
• @KYson I mean that the distribution has some value of standard deviation. I can get some approximation to that value by pulling a number of points out of it and taking their standard deviation. But the number that I get won't be the exact S.D. of the entire distribution. But surely, I am thinking, it must be good to within some "window". What is "the width of that window"? – bob.sacamento Apr 30 '15 at 18:11
• You said about a hundred points. Can use central limit theorem? – velut luna Apr 30 '15 at 18:26
• @Kyson I've looked it over. I don't see how I can use it. I'm admittedly not the sharpest tool in the shed sometimes. Any details you are thinking about will be most welcome. – bob.sacamento Apr 30 '15 at 20:01

If you want to find out the uncertainty or standard error (SE) in the standard deviation of a chosen sample, then you can simply use $SE(\sigma) = \frac{\sigma}{\sqrt{2N - 2}}$, where $N$ is the number of data points in your sample.

Hope that helps!

If you're allowed to take that sample repeatedly, it's basically bootstrapping.

Procedure:

1. Draw 100 points

2. Calculate standard deviation

3. Repeat Steps 1 & 2 a lot of times (empirically, I've found 5-10,000 to be enough), keeping track of the results of step 2.

4. Examine the distribution of estimates from Step 2 with whatever tools you'd like -- histograms, sample moments, etc.