Calculating the Percentage Error in Measurement Uncertainties

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Homework Help Overview

The discussion revolves around calculating the percentage error in the difference of two measurements, x and y, each with associated uncertainties. The original poster presents the values and uncertainties, seeking guidance on how to proceed with the calculation of percentage error.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the nature of the uncertainties and whether the measurements x and y are independent. There is a mention of using standard errors and formulas related to the propagation of uncertainty in calculations involving addition and subtraction.

Discussion Status

Some participants have provided insights into the treatment of uncertainties and the implications of measurement independence. There is an ongoing exploration of the rules for combining uncertainties in different mathematical operations, but no consensus has been reached on the specific application to the original problem.

Contextual Notes

The original poster's understanding of how uncertainties interact in calculations is questioned, and there is a reference to prior knowledge of error theory that may be relevant to the discussion.

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Homework Statement


Let x=5.234+/-0.0005 and y=5.123+/-0.0005. Find the percentage error of the difference a=x-y when relative errors deltax=deltay=0.0001.

Homework Equations


I think a=0.111 because 5.234-5.123=0.111 and +/-0.0005 cancel each other out. But how do I find the answer from here?


The Attempt at a Solution

 
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The plus-and-minuses don't cancel each other out.

Are x and y independent?
You must have done some theory of errors before now?

I prefer sigmas for standard errors:
$$\sigma_a^2=\sigma_x^2+\sigma_y^2\\ p_a=100\frac{\sigma_a}{a}$$
 
Thanks.
 
If x and y are measurements of properties X and Y, then we can represent uncertainties in the measurement process by writing: $$X=x\pm\sigma_x\qquad Y=y\pm\sigma_y$$.
When we do, we are basically saying that X and Y can be modeled as continuous random variables which are normally distributed with mean equal to their measured value and standard deviation equal to the uncertainty.

When you deal with them like that, then all that stuff you probably did in secondary school about hypothesis testing comes into play.

X and Y may have dependent of independent measurements.

If independent then:$$z=x+y\implies \sigma_z^2=\sigma_x^2+\sigma_y^2\\
z=xy\implies \left(\frac{\sigma_z}{z}\right)^2=\left(\frac{\sigma_x}{x}\right)^2+ \left( \frac{\sigma_y}{y}\right)^2$$note that ##x-y=x+(-y)## and ##x\div y = x\times (1/y)## so I don't need to provide specific rules for subtraction and division: the uncertainties part always adds.
BTW: If you think these rules look like Pythagoras - you are right.

If the measurements are dependent though - like we measure length y starting from where length x left off or we add/multiply a measurement to itself - then the rules get looser:$$z=x+y\implies \sigma_z=\sigma_x+\sigma_y\\ z=xy\implies \frac{\sigma_z}{z}=\frac{\sigma_x}{x}+\frac{\sigma_y}{y}$$

These are rules of thumb - from them, the others can be derived.
These rules come from a huge body of math that is often covered at senior undergrad and/or junior postgrad levels in science courses. Below that just remember the rules.
 

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