Calculating Mean Value Error: Tips for Undergrad Lab Work | Matt's Question

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To calculate the error in a mean value from individual measurements, determine the lowest and highest possible means based on the errors of each value. For independent measurements with Gaussian uncertainties, the variance of the sum is the sum of the variances, expressed as σ_z = √(σ_1² + σ_2²). This method allows for the calculation of the mean error by finding the range between the lowest and highest means. However, there is a concern that this approach may overestimate the error due to the nature of Gaussian distributions. Understanding these calculations is essential for accurate data analysis in undergraduate lab work.
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I'm an undergrad doing labs, and I was wondering how to get the error in a mean value, given that I know the errors of each individual value? This is probably a simple question, but any help is appreciated. Thanks, Matt.
 
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Try looking at the errors of each individual value and find the lowest possible number for each, and find the mean of that. Then do this again for the highest possible number. Then you have the lowest possible mean, and the highest possible mean, and from that you can find the error.
 
for independent measurements, with gaussian uncertainties: the variance of the sum is the sum of the variances.
so for:

z=x_1+x_2 means \sigma_z=\sqrt{\sigma_1^2 + \sigma_2^2}

for the mean you have N of them: can you wrok it out now?
(good practice - and it builds character xD)

@yortzec: I would have thought gaussian uncertainties would be more tightly distributed than that - won't that method give an over-estimate?
 
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