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I have seen that "the best estimate for the random error σ(X) in a single measurement is given by
σ(X)2 ≈ 1/(n-1) * ∑((xi-μ)2) where the sum is over all i"
I have two questions about this: firstly, how can this pertain to a "single measurement" if it requires the data from multiple measurements (x1, x2, x3, ... xi)? Secondly, this seems to correspond to the sample variance - wouldn't it be a more accurate estimate of the value of X's random error to convert to the variance of the population of X as a whole?
σ(X)2 ≈ 1/(n-1) * ∑((xi-μ)2) where the sum is over all i"
I have two questions about this: firstly, how can this pertain to a "single measurement" if it requires the data from multiple measurements (x1, x2, x3, ... xi)? Secondly, this seems to correspond to the sample variance - wouldn't it be a more accurate estimate of the value of X's random error to convert to the variance of the population of X as a whole?