Standard Deviation questions.

[AaronBrown]

I concur 100%, but I add one caveat. That works as long as you're looking for 90% confidence or less. Once you get above that, the violations of assumptions can kill you.

When people talk about 6-sigma performance, meaning 6 standard deviations above the mean, meaning 1 chance in a billion; I say if you think you've achieved it you've underestimated sigma. 6 people in the world can call me a liar, I don't think I've met any of them yet.

[BillC]

The variance follows a chi-square distribution (under some assumptions). If you just want answers, look at an elementary stat book, e.g., Triola. I did post some such numbers a long while ago.

The upshot is that estimating your variance takes a lot less than for your EV, so don't sweat it.
The t-statistic does not seem really relevant here.
Do not become obsessed with variance. If you do, the crazies will get you.

[BillC]

For example, to estimate sigma to within 5% with 95% confidence, you need a sample of at least 767 (hours).

Change 5% to 10%, you need only n=191.

These numbers assume a underlying normal population.