Calculating Average Error Uncertainty from a Large Set of Data

AI Thread Summary
To calculate the average error uncertainty from a large set of data with unique error uncertainties, one can sum all the absolute error values and divide by the total number of data points. The formula for standard deviation is also discussed, but the focus is on averaging the errors directly. It's important to ignore negative signs when summing the errors. This method provides a straightforward approach to determining the average error for the sample. Understanding these calculations is essential for accurate data analysis in various fields.
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Homework Statement



Hi, I have this question that is bothering me. If I have a large set of data, each with its unique error uncertainty. How do I get the average error uncertainty from all the data points? Do I simply use the equation below:

(∆ Z) ² = (∆A)² + (∆B)²

And divide the error obtained from this by the total number of errors combined?

Thanks.
 
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As in you want to find the standard deviation for a sample?


s^2=\frac{\sum_{i=0} ^N (x_i -\bar{x})^2}{N-1}
 
rock.freak667 said:
As in you want to find the standard deviation for a sample?


s^2=\frac{\sum_{i=0} ^N (x_i -\bar{x})^2}{N-1}


I want to find the average error of the sample, given that every value has its own different error uncertainty.
 
The average error is just the average of the errors.
Add all the errors together ignoring the minus signs, and divide by the number of values.
Is that waht you mean?
http://www.rit.edu/cos/uphysics/uncertainties/Uncertaintiespart1.html#estimate
 
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