Statistical Error of Centroid of Gaussian Distribution

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SUMMARY

The discussion focuses on deriving the expression for the "origin estimation error" of the centroid of a 3D Gaussian distribution as a function of the number of samples, L. It is established that as L approaches infinity, the error approaches zero, specifically following the relationship of error decreasing as 1/sqrt(L). The proof for this behavior is linked to the standard deviation of the mean, with a reference to J.R. Taylor's "An Introduction to Error Analysis" for further details on the topic.

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Statistical "Error" of Centroid of Gaussian Distribution

If I have L data samples, distributed randomly (3D real Gaussian distibution, unity variance) about the origin in 3D real space, how can I derive an expression for the "origin estimation error" (i.e. the distance between the true origin and the centroid of the data points) as a function of L?

Intuitively, as L->infinity, the error->0. In fact, it is easy to show in Matlab that the error falls as 1/sqrt(L) (for sufficiently large number of trials). However, I don't know where to start with a proof. (I'm really trying to write a proof for N-dim complex space, but I expect that will only need an extra sqrt(2) term).

Any advice is much appreciated!
 
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Problem solved:

Of course, I'm simply looking for the standard deviation of the mean. A proof for its behaviour as a function of number of samples can be found in:

J.R. Taylor, "An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements", pp. 147-148.
 

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