Statistical uncertainty of weighted mean

AI Thread Summary
The discussion focuses on the statistical uncertainty associated with calculating a weighted mean from binned measurements in a particle analysis. The user, Brais, observes that combining binned results yields a standard deviation approximately half that of the overall calculation, raising questions about the validity of the method used. Responses suggest that Brais may be misapplying a formula intended for known standard deviations, as the errors derived from the minimization algorithm may not accurately represent the true uncertainties. Clarification on the definitions and calculations of standard deviations in this context is recommended. The conversation highlights the complexities of statistical analysis in physical data measurements.
Brais
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Hello!

I am using physical data to do an analysis (~30k measurements). These measurements include energies, momenta, angles... of particles.
I am calculating a value (call it v) at the end after a lengthy process, and if I introduce all the data into my program I did, the result is v±σ.
If, however, I "bin" my events in energy and angle (say I made four bins in total), when I calculate "v", I get v1±σ1, v2±σ2, v3±σ3, v4±σ4. Then I combine these values into one using a weighted average (c stands for combined): v_c = \sum(v_i/\sigma^2_i)/\sum(1/\sigma^2_i), and \sigma_c = 1/\sqrt{\sum(1/\sigma^2_i)} (as can be seen here).
When I do this, it turns out that \sigma_c \simeq \sigma/2. How can this be? I am using the same amount of statistics!

Any reply or idea will be very welcome!

Thank you!

Brais.
 
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Hey Brais.

The wiki article looks straight-forward, but perhaps you could just outline your calculations in a little more detail step by step to show the simplifications and assumptions you used.
 
Hi, thanks for your reply!
I am calculating a fit. If I put all my data together I get an error that is higher than that of fitting different sets of points separately and then combining them with a weighted mean.
I didn't do any simplification, just applied the expression seen in wikipedia.

Brais
 
Brais said:
I didn't do any simplification, just applied the expression seen in wikipedia.
Brais

You are using a formula from that article that applies when you know the standard deviations of the distributions that are involved, but I'd guess that you don't. Your \sigma_i are probably estimators of standard deviations that you computed from the sample. (The term "standard deviation" is ambiguous. It has at least 5 different meanings in statistics, depending on the context where it appears.)
 
Following Stephen Tashi's post, you should probably just clarify exactly what attribute you are using.
 
Hi again!

A long time ago I had to stop this analysis and so my doubt wasn't importantr for some time :)
I use the errors that my minimization algorithm "MINUIT" gives. Unfortunatelly I cannot find anything except that it (obviously) calculates a covariance matrix and error matrix...

Thanks,

Brais
 
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