What is the difference between a measured value and an accepted value? and how are these values used to determine the precision of your answer?
measured is what you measured.
accepted value is what is generally accepted as "true".
Precision= (actual-accepted)*100/accepted in percent.
thank you bobpursely! I appreciate your help. God bless :-)
The difference between a measured value and an accepted value lies in their definitions and purposes. A measured value is obtained through an experimental or observational process, where an actual physical quantity is quantified using a measuring instrument or technique. On the other hand, an accepted value is a reference value that is widely recognized, established by previous experiments, scientific consensus, or sometimes defined by a standard or a theoretical calculation.
To determine the precision of your answer, you need to compare your measured value to the accepted value. Precision refers to the degree of agreement or consistency between multiple measurements of the same quantity. It characterizes the closeness of the measured values to each other and to the true or accepted value.
To evaluate precision, you can calculate the difference or the deviation between the measured value and the accepted value. This deviation can be positive or negative, depending on whether the measured value is higher or lower than the accepted value. By taking multiple measurements and calculating the average deviation, you can estimate the precision of your mean value.
Furthermore, to express the precision more quantitatively, you can calculate the absolute or relative uncertainty associated with each measured value. Absolute uncertainty is simply the difference between the measured value and the accepted value, while relative uncertainty is the absolute uncertainty divided by the accepted value, expressed as a percentage.
By comparing the magnitudes of these uncertainties, you can make a judgment about the precision of your measurements. Smaller uncertainties indicate higher precision, whereas larger uncertainties suggest lower precision. Additionally, statistical measures such as standard deviation or standard error can be used to further quantify the precision of your data.