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 consistently occur in the same direction every time a measurement is taken. These errors can be due to faulty equipment, calibration issues, or environmental factors. Random uncertainty, on the other hand, refers to errors that vary in an unpredictable manner each time a measurement is taken. These can arise from unpredictable fluctuations in experimental conditions or limitations in the measurement process.
Systematic uncertainty can be minimized by carefully calibrating instruments, using high-quality equipment, and controlling environmental conditions. Regularly checking and adjusting instruments, using standard reference materials, and applying correction factors can also help reduce systematic errors.
Common sources of random uncertainty include thermal noise in electronic circuits, fluctuations in environmental conditions (such as temperature and humidity), and human error in taking measurements. Inherent limitations of measurement instruments, such as the resolution of a digital display, can also contribute to random uncertainty.
Systematic uncertainties are quantified by identifying and estimating the potential bias in measurements, often through repeated calibration and comparison with known standards. Random uncertainties are quantified by statistical analysis, typically using the standard deviation or variance of a series of repeated measurements. Combining these uncertainties gives an overall uncertainty for the measurement.
Distinguishing between systematic and random uncertainty is crucial because they have different implications for the accuracy and reliability of measurements. Systematic uncertainty can lead to consistent bias, potentially invalidating results if not corrected. Random uncertainty affects the precision of measurements and can be reduced by averaging multiple measurements. Understanding both types of uncertainty helps in designing better experiments and improving measurement accuracy.