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.
Systematic uncertainty refers to errors that occur consistently in the same direction, while random uncertainty refers to errors that occur randomly and cannot be predicted. Systematic uncertainty can be reduced by identifying and correcting the source of error, while random uncertainty can only be reduced by increasing the precision of measurements.
Systematic uncertainty is calculated by finding the difference between the measured value and the true value, while random uncertainty is calculated by finding the standard deviation of a set of measurements. Both types of uncertainty can be represented by error bars on a graph.
Uncertainty is an inherent part of scientific research and can affect the accuracy and reliability of results. It is important for scientists to understand and account for uncertainty in their experiments and analyses in order to draw valid conclusions.
Uncertainty can be minimized by carefully designing experiments, using precise and accurate instruments, and repeating measurements multiple times. It is also important to identify and account for sources of systematic uncertainty.
Uncertainty can affect the interpretation of data by introducing a margin of error and making it difficult to determine the true value. It is important for scientists to report and consider uncertainty when interpreting their data in order to accurately communicate the limitations of their findings.