How Do You Calculate the Mean and Its Standard Deviation in Error Analysis?

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To calculate the mean and its standard deviation in error analysis, the mean is determined using the formula \(\bar{x} = \sum_{i=1}^{N} w_{i} x_{i}\), where \(w_{i} = \left( \frac{\sigma}{\sigma_{i}} \right)^2\). The standard deviation of the mean is given by \(\sigma_{\bar{x}} = \sigma = \frac{1}{ \sqrt{ \sum_{i=1}^{N} \frac{1}{\sigma_{i}^{2}} } }\). In this context, \(\sigma_{i}\) represents the uncertainty or standard deviation of each individual data point. It's important to maintain consistency in the use of \(\sigma_{i}\) across calculations, as any multiple will cancel out in the fraction. Understanding these formulas is crucial for accurate error analysis in lab reports.
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I'm doing a report on a set of lab data and am supposed to find the mean and mean's standard deviation

\bar{x} + \sigma_{\bar{x}}

The mean is given by

\displaystyle{ \bar{x} = \sum_{i=1}^{N} w_{i} x_{i} }

Where

\displaystyle{ w_{i} = \left( \frac{\sigma}{\sigma_{i}} \right)^2 }

and for the error (mean's standard deviation)

\displaystyle{ \sigma_{\bar{x}} = \sigma = \frac{1}{ \sqrt{ \sum_{i=1}^{N} \frac{1}{\sigma_{i}^{2} } } } }The problem is I can't quite figure it out what the σi would be, is it the standard deviation? This might sound very silly but it's been a long time I've dealt with this.

Any help is appreciated.
 
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σi is the uncertainty (standard deviation*) of data point i.

*any multiple of it will work as well, if you keep it consistent, as it cancels in the fraction
 

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