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  1. Problem: Suppose you measure three numbers as follows: x = 200§2; y = 50§2; z = 40§2; where the three uncertainties are independent and random. Use step-by-step propagation to find the quantity q = x=(y ¡ z) with its uncertainty. Solution: Let D = y¡z = 10§2 p 2 = 10§3. Then q = x D = 20§20 p 0:012 +0:32 = 20§6: 10/5/01 7

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  2. You could also report this same uncertainty as a relative error, denoted as ˙ rel(X). This is when you compare the size of your error to the size of the original quantity.1 The formula for relative error is: ˙ relX= ˙ X jXj (1) Thus, in the above example, your 1cm uncertainty on your 5:89m measure-ment would turn into a relative error of 0:0016.

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  3. An error is the difference between the measured value and the expected value of something (unavoidable). An uncertainty is a way of expressing or summarizing the error (unavoidable).

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  4. Knowing errors and uncertainties is an essential part for ensuring reproducibility. • To know the uncertainties we use two approaches: (1) Repeat each measurement many times and determine how well the result reproduces itself. If the results are different then there are statistical errors.

  5. Basic formula for propagation of errors The formulas derived in this tutorial for each different mathematical operation are based on taking the partial derivative of a function with respect to each variable that has uncertainty.

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  6. This may be due to natural variations, a lack of understanding of the process, or a simplified model in the theory. In this document, we discuss a few of these causes, and more importantly, how we can account for these problems and overcome them through careful collection, handling, and analysis.

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  8. Perhaps counterintuitively, a random error is usually easier to estimate than a systematic error. Random error is due to a combination of the limited precision to which a quantity can be read from a ruler or meter scale and intrinsic \noise" in the measurement.

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