Error Calculation for a Diffraction Grating's Performance

AI Thread Summary
To calculate the uncertainty of the mean N value for a diffraction grating, the discussion focuses on propagating errors from four average angle measurements. The proposed method involves summing the individual propagated errors in quadrature and dividing by the number of samples to find the average uncertainty. There is also a consideration of whether weighted standard deviation applies, particularly if the angular measurement errors are disproportionately affecting the results. Ultimately, the consensus leans towards using the quadrature method for this specific case. The participant plans to confirm their approach with their lecturer for further clarification.
zehkari
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Homework Statement


I need to calculate the error of an average value (N for diffraction grating).
My results were 4 different average angles. With which I calculated their uncertainty using std.
Using equation (1), I found the number of lines per meter (N) with a known wavelength (λ) and the correct diffraction order (p).
However, I now need an average of those 4 N values to find a mean N value.
What is my uncertainty on the mean N value, considering sin(θ) used in the equation is based off 4 results with their uncertainty?
I understand how to propagate error. I am just confused with how to take the 4 propagated errors based off the uncertainty of the angles to find an error on the mean N.

Homework Equations


(1) sin(θ)=pNλ

The Attempt at a Solution


My only idea would be adding all 4 propagation errors on N to give the total error for the average N?

Any help would be great, many thanks!
 
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Suppose that your N values are ##N_1, N_2,...N_4## with uncertainties ##\Delta_1,\Delta_2,...,\Delta_4##. I'd think that the uncertainties should sum in quadrature (square root of sum of squares) and be divided by the number of samples. So:

$$\Delta_{avg} = \frac{1}{4}\sqrt{\sum_{i=0}^4{\Delta_i^2}}$$
 
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what is your uncertainty in angle? You have a measurement equation, that determines your uncertainty.
 
gneill said:
Suppose that your N values are ##N_1, N_2,...N_4## with uncertainties ##\Delta_1,\Delta_2,...,\Delta_4##. I'd think that the uncertainties should sum in quadrature (square root of sum of squares) and be divided by the number of samples. So:

$$\Delta_{avg} = \frac{1}{4}\sqrt{\sum_{i=0}^4{\Delta_i^2}}$$

Hey, thanks, do you know of the weighted standard diviation? Does that apply here?
 
zehkari said:
Hey, thanks, do you know of the weighted standard diviation? Does that apply here?
I know of it. I'll state right away that I am not an expert in this area.

Having said that, I feel It might be applicable if the percent error in angular measurements is untowardly biasing the results (if Δθ is of fixed size then the fraction Δθ/θ becomes larger as θ gets smaller, even though you measure with the same accuracy).
 
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gneill said:
I know of it. I'll state right away that I am not an expert in this area.

Having said that, I feel It might be applicable if the percent error in angular measurements is untowardly biasing the results (if Δθ is of fixed size then the fraction Δθ/θ becomes larger as θ gets smaller, even though you measure with the same accuracy).

Yeah, the error in angle measurements is quite small. I think I will stick with sum of uncertainty in quadrature like you suggested and then talk it over with my lecturer. Thank you for your help.
 
You're very welcome.
 
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