Statistical uncertainty of weighted mean

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Discussion Overview

The discussion revolves around the statistical uncertainty associated with calculating a weighted mean from physical measurements, specifically addressing discrepancies in error estimates when combining data from multiple bins versus using all data collectively. The context includes theoretical and practical aspects of statistical analysis in physics.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Brais describes a scenario where a value (v) is calculated from approximately 30,000 measurements, resulting in a combined uncertainty when using a weighted average from binned data that is unexpectedly lower than when using all data.
  • Some participants suggest that Brais clarify the calculations and assumptions made during the analysis to better understand the results.
  • One participant points out that the formula used for combining uncertainties may not apply if the standard deviations are estimators derived from the sample, highlighting the ambiguity of the term "standard deviation" in statistics.
  • Brais mentions using errors provided by the MINUIT minimization algorithm, which involves a covariance matrix and error matrix, but expresses uncertainty about their implications for the analysis.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the reasons for the observed discrepancy in uncertainty estimates, and multiple viewpoints regarding the interpretation of statistical terms and methods remain present.

Contextual Notes

There are unresolved questions regarding the definitions and assumptions related to standard deviations and the appropriateness of the formulas applied in the analysis.

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): [itex]v_c = \sum(v_i/\sigma^2_i)/\sum(1/\sigma^2_i)[/itex], and [itex]\sigma_c = 1/\sqrt{\sum(1/\sigma^2_i)}[/itex] (as can be seen here).
When I do this, it turns out that [itex]\sigma_c \simeq \sigma/2[/itex]. 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 [itex]\sigma_i[/itex] 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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