I Why Does Subtractive Cancellation Affect Measurement Precision?

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Subtractive cancellation, also known as significance loss, significantly affects measurement precision when subtracting values that are similar in magnitude, leading to increased error. This issue arises because the uncertainty in measurements can be amplified when the values are close, unlike in addition where errors do not compound in the same way. The phenomenon is often discussed in terms of propagation of errors or propagation of uncertainty, though specific references to subtractive cancellation in experimental contexts are less common. The relative uncertainty becomes larger in subtraction due to the smaller estimate resulting from the operation. Understanding this effect is crucial for accurate data analysis in experimental measurements.
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When subtracting measurements that each have a certain level of uncertainty, we can end up with huge levels of error if the two measurements are roughly equal in value. However, this problem doesn't appear when adding numbers.

Question: Is there a name for this type of error? Does anyone discuss the problems associated with subtracting measurements that are similar in magnitude? I can't seem to find anything on this source of error, possibly because I don't know what it's called.

In numerical analysis, they call this subtractive cancellation or significance loss and the uncertainty in the numbers is due to the truncation of numbers to fit in a computer's memory. But I cannot find anything on this effect for experimental measurements.
 
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It is usually called the propagation of errors or propagation of uncertainty. The uncertainty is not larger from subtracting rather than adding. The difference is the size of the estimate. This makes the relative uncertainty larger because the relative uncertainty is the uncertainty divided by the estimate.
 
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