Large Uncertainty Became Unsignificant After Calculations?

AI Thread Summary
The discussion centers on calculating the focal length of lenses using given measurements with uncertainties. The focal length is derived from the formula f = (s)(s') / (s + s'), where s and s' have associated uncertainties. The user questions why the initially significant uncertainty in measurements (5.52 mm) results in a much smaller uncertainty (0.02412 mm) in the final focal length calculation. Clarifications are provided on how to properly apply uncertainty rules for multiplication and division, emphasizing that proportional errors remain consistent even when inverting fractions. The conversation concludes with guidance on correctly calculating the uncertainty in the focal length.
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Homework Statement



Calculating focal lengths of lenses.

s = 144.7 ± 5.52 mm
s' = 86.0 + 0.71 mm

Homework Equations

Focal Length
f = (s)(s') / ( s + s' )

Adding/Subtracting two numbers with uncertainties
A + B ± root [ (±A)^2 + (±B)^2 ]

Multiplying/Dividing

AB ± root [ (±A/A)^2 + (±B/B)^2) ]

The Attempt at a Solution



I have:

s = 144.7 ± 5.52 mm
s' = 86.0 + 0.71 mm

and am getting:

f = 53.94 ± 0.02412 mm

My question is, does it make sense that the uncertainty that was obviously significant at the start (5.52mm uncertainty in a 144.7mm magnitude) became so miniscule at the end? The way I am calculating this is

1) Calculating numerator and denominator of focal length equation separately
-Top = ( s * s' ) and get uncertainty of 0.039mm using the uncertainty multiplication rule
-Bot = ( s + s' ) and get uncertainty of 5.57mm using the uncertainty addition rule.

2) Then I divide the top by the bottom apply the uncertainty division rule.

Am I doing everything correctly?
 
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It can be smaller in absolute terms but I'd still expect it to be of the order of 1%.
You need to check the calculation ...
 
You have a relation of the form \frac{1}{z} = \frac{1}{x} + \frac{1}{y} \Rightarrow z = \frac{xy}{x+y}... and you want to find z \pm \sigma_z given x \pm \sigma_x and y \pm \sigma_y

rule: proportional errors do not change with inverting the fractions

hence\frac{\sigma_{1/x}}{1/x} = \frac{\sigma_x}{x} \Rightarrow \sigma_{1/x} = \frac{\sigma_x}{x^2}... and the same for \sigma_{1/y}

rule: the variance of the sum is the sum of the variances
\Rightarrow \sigma_{1/z}^2 = { \sigma_{1/x}^2 + \sigma_{1/y}^2}
rule: proportional errors do not change with inverting the fractions
\frac{\sigma_z}{z} = \frac{\sigma_{1/z}}{1/z} \Rightarrow \sigma_z = z^2\sigma_{1/z}

... you should be able to take it from there.
 
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