Thanks!FactChecker said:I'm not exactly sure about your terms, but here is my two cents:
A systemic error may be very consistent and predictable. That makes it conceivable to determine the error and make adjustments to the measured value and get the true value. On the other hand, a truly random error is difficult to determine and you can not make adjustments to the measured value.
A systemic error may just repeat the same error over and over, so taking the average of multiple readings will not reduce the error.
Fractional uncertainty is a measure of the uncertainty or error associated with a measurement or calculation. It is expressed as a fraction or percentage of the measured value, and it indicates how much the measured value might deviate from the true value.
Fractional uncertainty is calculated by dividing the uncertainty of a measurement by the measured value and then multiplying by 100 to express it as a percentage. For example, if a length is measured to be 10.5 cm with an uncertainty of 0.1 cm, the fractional uncertainty would be (0.1/10.5) * 100 = 0.95%.
Fractional uncertainty is important because it gives an idea of the accuracy and reliability of a measurement. It also helps in comparing different measurements with varying degrees of uncertainty and determining which one is more precise.
Fractional uncertainty affects the validity of a calculation by propagating through any mathematical operations performed on the measured values. This means that the fractional uncertainty of the final result will be the combination of the uncertainties from all the measured values involved in the calculation.
Some strategies for reducing fractional uncertainty include using more precise measurement instruments, taking multiple measurements and averaging the values, and minimizing sources of error in the measurement process. It is also important to properly estimate and account for uncertainties in the measurement process.