Determine the number of required Monte Carlo iterations

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To determine the minimum number of Monte Carlo iterations for accurate reporting of the standard deviation of miss angles in a weapon system simulation, a 95% confidence level is required with an accuracy of 0.01 milli radians. The uncertainty on the standard deviation decreases with the square root of the number of iterations, which means more iterations will yield a more precise estimate. The specific number of iterations needed can be calculated using the standard deviation of the miss angles and a numerical prefactor from statistical references. It's essential to ensure that the initial standard deviation estimate is reliable to inform the required sample size accurately. Proper statistical methods will guide the determination of the necessary iterations for the simulation.
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I am performing a Monte Carlo simulation of a weapon system to determine the effect of variations in weapon characterisitics on the accuracy of the weapon. For example, I might vary the muzzle velocity of the weapon and measure the angles between the aim point and the impact point (i.e. miss angles).

I need to report the standard deviation of the miss angles in milli radians to two decimal places, so I would like to determine the minimum number of Monte Carlo iterations required to obtain a 95% confidence level that the standard deviation of the miss angles are accurate to 0.01 mili radians.

My statistics are a bit rusty, so does anyone know of the best way to go about this?
 
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The uncertainty on the standard deviation goes down with the square root of the number of data points (as usual), so it depends on the size of your standard deviation and some numerical prefactor you can look up.
 
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