Instrument Error or Standard Deviation

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To perform error analysis on distance measurements, two main methods for calculating relative error are discussed: using instrument uncertainty divided by the mean measurement, and calculating absolute error via standard deviation. The instrument uncertainty method yields a straightforward estimate, while the standard deviation approach accounts for variability in the data. A common practice is to use the larger of the two values to ensure a more conservative estimate of error. Additionally, it's recommended to calculate error directly from the dataset, applying the formula for standard deviation with n-1 for accuracy. This allows for a more nuanced analysis, including the application of criteria like Chauvenet's criterion for outlier detection. Understanding the distinction between random and systematic errors is crucial for rigorous error analysis.
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I had a question about how to properly perform error analysis on a series of distance measurements. I need to find the relative error in the distance. I was wondering, should I use the instrument uncertainty divided by the measurement (in this case, it would be 0.005m / (mean measurement)). Or, should I be using the formula that relates absolute error to standard deviation: σx=sx/(N1/2), then use this value to find relative error. Or, should I use whichever one is larger to account for the largest possible error?
 
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no_face said:
I had a question about how to properly perform error analysis on a series of distance measurements. I need to find the relative error in the distance. I was wondering, should I use the instrument uncertainty divided by the measurement (in this case, it would be 0.005m / (mean measurement)). Or, should I be using the formula that relates absolute error to standard deviation: σx=sx/(N1/2), then use this value to find relative error. Or, should I use whichever one is larger to account for the largest possible error?

Both are reasonable ball park estimates, and using the larger one is a common approach. There are subtleties regarding random and systematic errors which prevent either from being rigorous.
 
no_face said:
I had a question about how to properly perform error analysis on a series of distance measurements. I need to find the relative error in the distance. I was wondering, should I use the instrument uncertainty divided by the measurement (in this case, it would be 0.005m / (mean measurement)). Or, should I be using the formula that relates absolute error to standard deviation: σx=sx/(N1/2), then use this value to find relative error. Or, should I use whichever one is larger to account for the largest possible error?

If you have a data set I always recommend calculating error from it directly. You should find ##\alpha = \frac{σ_{n-1}}{{n^{1/2}}}##, remember to calculate the standard deviation as n-1, you lose a degree of freedom when working from the set. You can then analyze the data, apply Chauvenet's criterion, relative error, etc.
 
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