The discussion clarifies the relationship between absolute uncertainty, systematic uncertainty, and random uncertainty. Absolute uncertainty is defined as the total amount by which a measured value may differ from the actual value, contrasting with fractional uncertainty, which is the ratio of absolute uncertainty to the measured value. Total error is expressed as the sum of systematic and random errors, each of which can be interpreted as either fractional or absolute. The conversation also highlights that these errors behave differently when measuring the same quantity versus varying parameters.
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BvU said:Any particular reason ?
Thank you for your replies @BvU and @haruspex!haruspex said:As far as I am aware, absolute uncertainty means the absolute amount by which the measured value may differ from the actual value. This is as opposed to fractional uncertainty, which is absolute uncertainty divided by the measured value.
And I find "systematic uncertainty" conceptually awkward. Systematic error is the more usual expression.
So I would say that total error is systematic + random, where each of those may be (consistently) interpreted as fractional or absolute.
That is with regard to repeated measurements which are in principle of the same quantity. If they are for different quantities (because some parameter is being varied) these errors may vary in different ways. E.g. the systematic fractional error my remain constant, while for random error it is the absolute error that is constant.