Standard deviation from measures with different uncertainty

In summary, the conversation discusses the calculation of standard deviation and final result of a measurement with varying uncertainties. The individual results are given as L=5\pm0,2, L=5,1\pm0,1,L=5,2\pm0,3,L=5,3\pm0,1,L=5,4\pm0,2 and the question is how to handle the different uncertainties. The suggested approach is to first calculate the arithmetic mean, variance, and standard deviation of all results. However, the question remains as to how to incorporate the varying uncertainties into the final result.
  • #1
player1_1_1
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Homework Statement


i have a few results of measurements, with different measurement uncertainty: [tex]L=5\pm0,2, L=5,1\pm0,1,L=5,2\pm0,3,L=5,3\pm0,1,L=5,4\pm0,2[/tex] and how can i count standard deviation and final result of measurement with uncertainty?

The Attempt at a Solution


i counted arithmetic mean, variance and standard deviation of all results, if they were the same I would just do this like this [tex]\Delta L=\sqrt{V(L)+U^2(L)}[/tex] where [tex]U(L)[/tex] is measurement uncertainty of a single result, but what to do when they are different?
 
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  • #2
There's a good explanation of how to treat the uncertainty here
http://www.rit.edu/cos/uphysics/uncertainties/Uncertaintiespart1.html#estimate
 
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  • #3
thanks for this link, it is very helpful, but there arent solution for what to do with this thing that i mentioned, do you have any other idea?
 

What is standard deviation from measures with different uncertainty?

Standard deviation from measures with different uncertainty is a statistical measure that quantifies the amount of variation or dispersion of a set of data points from their mean value. It takes into account the uncertainty or error associated with each individual measurement to provide a more accurate representation of the data.

How is standard deviation from measures with different uncertainty calculated?

Standard deviation from measures with different uncertainty is calculated by first finding the mean value of the data points. Then, for each data point, the distance from the mean is squared and added together. This sum is then divided by the total number of data points minus 1, and the square root of this value is taken to obtain the standard deviation.

Why is standard deviation from measures with different uncertainty important?

Standard deviation from measures with different uncertainty is important because it provides a more accurate representation of the data by taking into account the uncertainty associated with each measurement. It allows for a better understanding of the variability of the data and can help identify any potential outliers or errors in the measurements.

How does standard deviation from measures with different uncertainty differ from standard deviation of a single measure?

The standard deviation from measures with different uncertainty differs from the standard deviation of a single measure in that it takes into account the uncertainty or error associated with each individual measurement. This means that it provides a more precise measure of the variability of the data and is a more accurate representation of the data set.

Can standard deviation from measures with different uncertainty be negative?

No, standard deviation from measures with different uncertainty cannot be negative. Standard deviation is always a positive value as it represents the distance from the mean. If a negative value is obtained, it is likely due to an error in the calculations or data.

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