In expressing experiential error or uncertainty, when does one use (a) experimental error and (b) percentage difference

In expressing experiential error or uncertainty, you can use two common measures: (a) experimental error and (b) percentage difference. Let's take a closer look at each one and when to use them:

(a) Experimental error:
Experimental error is a measure of the consistency and accuracy of your experimental results. It compares the measured value to the accepted or theoretical value and provides insights into the quality of your measurements. It helps to evaluate how well you conducted the experiment and identify potential sources of error. The formula to calculate experimental error is:

Experimental error = |Measured value - Accepted value|

Use experimental error when you want to assess the accuracy of your experimental results compared to the accepted or theoretical value. It is often expressed as an absolute value since it represents the magnitude of the deviation.

(b) Percentage difference:
Percentage difference is another way to express the deviation or discrepancy between two values. It represents the difference between two values as a percentage of their average. The formula to calculate percentage difference is:

Percentage difference = |(Measured value - Accepted value) / Average of the two values| * 100

Use percentage difference when you want to express how much the measured value deviates from the accepted value relative to their average. It provides a quantitative measure of the relative discrepancy between the two values.

Both experimental error and percentage difference can be useful in expressing experiential error or uncertainty, but the choice depends on the context and the information you want to convey. If you want to emphasize the magnitude of the deviation, use experimental error. If you want to highlight the relative discrepancy as a percentage, use percentage difference.

% difference? Please give an example of what you want to calculate. Is that for one problem or two kinds of problem. Personally, I think you may be over thinking the problem.