So the scenario is - <br/>
I have a random variable X of which I sample N values [$x_{1}$...$x_{N}$]. From these values I calculate the estimate P of function H(x) using Importance Sampling, i.e. $P = \sum_{i=1}^{N} w_{i}H(x_{i})$<br/>
( $H(x_{i})$ gives as output either 0 or 1) .<br/>
This is done for T trials and for each trial ' $t$ ',  I have two output vectors - <br/>
1) [$P_{0}$, $P_{1}$, ..., $P_{t}$] , and <br/>
2) [$Pavg_{0}$, $Pavg_{1}$, ..., $Pavg_{t}$] where $Pavg_{i}$ is the average of all P's upto trial t i.e. $Pavg_{i}= \sum_{k=1}^{t}P_{k}$

Question is what is the variance of $Pavg_{t}$. I want this value so that I can know how close my simulation results are to the actual case. Should I just calculate the variance($Pavg_{t}$) from the vector(2)?<br/>
I also came across the formula to calculate variance of estimate in Monte Carlo, given as $Var(P_{MC}) = P_{MC}*(1-P_{MC})/N$, where $P_{MC}$ is the monte-carlo estimate.
Do I use this to calculate $Var(Pavg_{t})$?