BvU said:No. This way you do injustice to accurate measurements and you let bad measurements have too much weight.
There are simple rules for this. The average isn't the best estimate: that is the http://www.colorado.edu/physics/phys2150/phys2150_sp14/phys2150_lec4.pdf.
Loved the Dilbert comic in that link.BvU said:No. This way you do injustice to accurate measurements and you let bad measurements have too much weight.
There are simple rules for this. The average isn't the best estimate: that is the http://www.colorado.edu/physics/phys2150/phys2150_sp14/phys2150_lec4.pdf.
Average value uncertainty is a measure of the amount of uncertainty in a set of data or measurements. It represents the range of values that the true value of a quantity could fall within, taking into account all sources of error and variability in the data.
Computing average value uncertainty allows scientists to understand the reliability and accuracy of their data. It also helps to determine the precision of measurements and identify any sources of error that may be present.
The formula for calculating average value uncertainty is: uncertainty = (maximum value - minimum value) / 2. This takes into account the range of values in the data set and divides it by two to represent the uncertainty in the middle of the range.
There are several factors that can contribute to average value uncertainty, including instrumental error, human error, and natural variability in the data. It is important to identify and minimize these sources of uncertainty to improve the accuracy and reliability of data.
To reduce average value uncertainty, scientists can use more precise instruments, improve measurement techniques, and increase the number of measurements taken. It is also important to identify and control any sources of error and variability in the data.