Uncertainty Calculations for Electrical Measurements

In summary, division can have an asymmetric effect on uncertainty and can result in infinite uncertainty if the denominator has an interval that includes 0. However, if 0 is not included in the interval, the arithmetic can be applied in a straightforward manner. For future reference, the general method for dividing by an interval is to find the minimum and maximum quotient values by dividing by the maximum and minimum values of the denominator, respectively. There may be discipline-specific conventions, but the subject is known as interval arithmetic. Additionally, as long as the relative uncertainty is not too large, the uncertainty contribution of a variable can be estimated using the formula: uncertainty on the variable multiplied by the derivative of the function with respect to that variable. This method may fail if
  • #1
CookieSalesman
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I know the rules to experimental uncertainties with addition and subtraction, but what about division?

For instance here
The light bulb was measured to have 1.27 ±.05V by the DMM in parallel. Using the DMM in series, its current drawn was .202 ±1A. As a result its resistance was approximately 6.28±.05V/A.

I'm not totally sure if I've done it right...

Is there any large reference book for all of these rules for future insight?
 
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  • #2
Division will have an asymmetric effect on uncertainty. You can understand the uncertainty calculations in one of two ways, either as a statistical expression (this is the mean plus or minus some multiple of the standard deviation) or as an interval calculation (the value is in the interval centered at this number plus or minus the interval radius).

In the latter case dividing by an uncertain quantity can yield infinite uncertainty: If b = 0.2 +/- 0.3, and a= 3 then a/b is somewhere below -30 or above +6 since both the positive and negative sides of zero are possible in the denominator you can get numbers ranging to + or - infinity.

But as long as 0 is not in the interval you can apply the arithmetic in a relatively straight forward way. To divide by an interval figure out the minimum and maximum quotient values by respectively dividing by the max and min (note the reversal) values for the denominator. Then re-express in terms of a center plus or minus a radius. There may be discipline specific conventions but that is the general method so far as I know. The subject name is interval arithmetic searching that should give you all sorts of references.
 
  • #3
As long as the relative uncertainty is not too large, you can always estimate the uncertainty contribution of x to f(x,y,z,...) as
$$\Delta_x f(x,y,z,...) = \Delta x |\frac{df(x,y,z,...)}{dx}(x)|$$
where ##\Delta x## is the uncertainty on x, df/dx is the derivative with respect to x (evaluated at your central value for x), and ##\Delta_x f## is the uncertainty on f coming from the uncertainty on x. Multiple independent uncertain input parameters can be evaluated individually and added in quadrature.

This fails if the uncertainty is too large, or if you have correlated uncertainties in the inputs, then you'll need more complex methods.
 
  • #4
Thanks guys
 
  • #5
All I was taught is that in uncertainty calculations the relative uncertainties are summed up for quotients and products. So it would be RUncertainty voltage + Runcertainty of current.
 

1. What is uncertainty in scientific measurements?

Uncertainty in scientific measurements refers to the inherent variability or margin of error in a measurement. It is the degree of doubt or lack of confidence in the accuracy of a measurement.

2. Why is it important to consider uncertainty in scientific calculations?

It is important to consider uncertainty in scientific calculations because it allows us to understand the limitations of our measurements and results. It also helps us to evaluate the reliability and validity of our conclusions.

3. How is uncertainty calculated?

Uncertainty is typically calculated using statistical methods such as standard deviation, confidence intervals, or propagation of error equations. These methods take into account the precision and accuracy of the measurement as well as any sources of error.

4. What are the sources of uncertainty in scientific measurements?

Sources of uncertainty in scientific measurements can include equipment limitations, human error, environmental factors, and inherent variability in the system being measured. It is important to identify and account for these sources to minimize uncertainty in the results.

5. How can uncertainty be reduced in scientific calculations?

Uncertainty can be reduced in scientific calculations by improving the precision and accuracy of the measurements, identifying and minimizing sources of error, and using appropriate statistical methods to account for uncertainty. It is also important to repeat measurements and calculate an average to reduce uncertainty.

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