PhizKid said:
Yes, I am trying to take the average of several of the same experiments.
When we took our measurements, we were told to just put the measurement down, and that was it. But when we read a measurement that was in between millimeter lines, we were told to add 0.5 mm to the measurement, then state ±0.5 mm after that measurement. But only if we measured in between the mm lines. Otherwise we didn't have to state the ± uncertainty or whatever.
The proper way to do it is to *always* read between the millimeter lines and estimate a number within those lines.
But let's not go into that.
Obviously they want you to take measurements with nice round millimeter numbers which is easier.
To find the uncertainty of "n" trials, the method is as follows:
1. Calculate the average.
2. Calculate the difference of each trial with the average.
3. Square those differences.
4. Add all the squared differences.
5. Divide the sum by (n - 1). In your case this is 3. The result is the so called "variance".
6. Take the square root, which will give you the uncertainty. This is also called the "standard deviation".
This is what your calculator will do for you.
Note that none of the uncertainties of the trials is used for this.
They would be neglected unless they are big enough to matter.
In your case they are not.
Seeing that you are supposed to round to millimeters I don't think you are supposed to take them into account.Anyway, if you do want to take them into account, it works like this.
To find the resulting uncertainty due to the uncertainties in your trials, you would use the following.
1. Square each uncertainty.
2. Add those squares.
3. Divide by n. In your case this is 4. Again this will give you a "variance".
As it is, "variances" are supposed to be added - not the uncertainties you have.
So you would add the variance due to the average, with the variance due to your trials.
Afterward take the square root and you have your resulting uncertainty.
In your case you will see that the contribution of your trials uncertainties is way less than the uncertainty you get from the average.
Actually, it could also be the other way around.