Calculating Mean Value Error: Tips for Undergrad Lab Work | Matt's Question

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SUMMARY

The discussion focuses on calculating the mean value error in undergraduate lab work, specifically when individual measurement errors are known. The method involves determining the lowest and highest possible means based on individual errors, allowing for the calculation of the overall error. For independent measurements with Gaussian uncertainties, the variance of the sum is the sum of the variances, expressed mathematically as σ_z = √(σ_1² + σ_2²) for two measurements. This approach is confirmed as a valid practice for obtaining accurate mean value errors.

PREREQUISITES
  • Understanding of Gaussian uncertainties in measurements
  • Familiarity with variance and standard deviation calculations
  • Basic knowledge of statistical methods for error analysis
  • Experience with independent measurements in experimental physics
NEXT STEPS
  • Research the application of Gaussian error propagation in experimental data analysis
  • Learn about calculating combined uncertainties in multiple measurements
  • Explore statistical software tools for error analysis, such as R or Python's SciPy library
  • Study the principles of variance and standard deviation in greater depth
USEFUL FOR

This discussion is beneficial for undergraduate students in physics or engineering, lab instructors, and anyone involved in experimental data analysis who seeks to understand error propagation and mean value calculations.

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Homework Statement


I'm an undergrad doing labs, and I was wondering how to get the error in a mean value, given that I know the errors of each individual value? This is probably a simple question, but any help is appreciated. Thanks, Matt.
 
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Try looking at the errors of each individual value and find the lowest possible number for each, and find the mean of that. Then do this again for the highest possible number. Then you have the lowest possible mean, and the highest possible mean, and from that you can find the error.
 
for independent measurements, with gaussian uncertainties: the variance of the sum is the sum of the variances.
so for:

[itex]z=x_1+x_2[/itex] means [itex]\sigma_z=\sqrt{\sigma_1^2 + \sigma_2^2}[/itex]

for the mean you have N of them: can you wrok it out now?
(good practice - and it builds character xD)

@yortzec: I would have thought gaussian uncertainties would be more tightly distributed than that - won't that method give an over-estimate?
 

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