What is the best way to combine uncertainties in measurements?

AI Thread Summary
To combine uncertainties in measurements effectively, one can average the individual measurements to find the mean value. The standard deviation of the mean can be calculated by dividing the standard deviation of each measurement by the square root of the number of measurements. However, there is debate over whether this method adequately accounts for the individual uncertainties associated with each measurement. A counterexample highlights that different sets of measurements can yield the same mean and standard deviation, despite differing uncertainties. For further reading, "Error Analysis" by Taylor is recommended as a resource on this topic.
Littlepig
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Homework Statement



Suppose I have N x_{i} measures with a given uncertainty \Delta x_{i}.

I want to have the best estimate for \bar{x} and its uncertainty \bar{\Delta x}


2. Homework Equations /3. The Attempt at a Solution

Well, I'm not exactly sure because or I can have a mean value of x_i and uncertainty given by standard mean and standard deviation/N formulas and I disregard the measurement uncertainties, or I use the (mean of the \Delta x_{i})/N to the uncertainty of \bar{x} and disregard how x_i are close to the mean \bar{x}.

Is there any way of joint both uncertainties together?

Even if you don't explain it in here, can you give me literature where you know where I find it?

Thanks,
littlepig
 
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To find x-bar, you just average the individual measurements; nothing fancy about that. To find the standard deviation of this mean, you divide the standard deviation of each measurement by the square root of N.
 
ideasrule said:
To find x-bar, you just average the individual measurements; nothing fancy about that.

I agree

ideasrule said:
To find the standard deviation of this mean, you divide the standard deviation of each measurement by the square root of N.

I don't agree and I counter example with this:

assume:
Example 1:
x_1=15, delta x_1=0.2
x_2=9, delta x_2=0.1
x_3=3, delta x_3=0.2

Example 2:
x_1=9.2, delta x_1=0.2
x_2=9, delta x_2=0.1
x_3=8.8, delta x_3=0.2

By your method, both mu and sigma are the same for both examples, however, I think we both agree that example 2 should have smaller uncertainty!
 
bump, At least give me a name of a book to search on...xD please...
 
I'm not really sure what you've done in example 1. You do want to look at the standard deviation in the mean, as ideasrule said. If you're confused see if you can find Error Analysis by Taylor.
 
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