In my experiment I compute the average latency of operations per second.
I would like to define N, i.e: how many times do I need to run my experiment to compute a close-to-real average latency?
I figured that I could apply the CLT here, because If I repeat the same experiment 1000 times and plot a histogram, I get a normal distribution curve.
Is the central theorem useful in my case? In the definitions I found, to be able to compute an estimated mean with a certain error, one needs to know the variance beforehand, and I don't know it.
Best Answer
Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using the upper bound in the formulas you found will be a bit of overkill, but it should satisfy your requirements.