My question is about the required number of simulations for Monte-Carlo analysis method, as far as I see for any allowed percentage error $E$ e.g. 5, required number of simulation is given by:

$$n=(100 \cdot z_c \cdot \text{std}(x)/(E \cdot \text{mean}(x)))^2$$

where $\text{std}(x)$ is standard deviation of resulted sampling, $z_c$ is confidence level coefficient e.g. for 95% it is 1.96, so in this way it is possible to check resulted mean and std of n simulations represent actual mean and std with 95% confidence level. In my case I run the simualtion 7500 times and create different mean and std of 100 sampling from 7500 simulation results (a moving average and std), even required number of simulation is always less than 100, but % error of mean and std compare to mean and std of entire results is not always less than 5%. % error of mean is less than 5% in most case but error of std goes up to 30%. What is the best way to determine number of required simulation without know actual mean and std? also in my case subjected outcome of simulation is normal distributed, thanks in advance for any help