Experimental Uncertainty

Perhaps a simpler explanation is that an average value can look like an exact figure and does not give the reader any understanding of the raw data. Does a reading of 1.38 mean the values were 1.38, 1.39, 1.38, 1.37, 1.38 or 1.10, 1.40, 1.60, 1,70, 1.10? Both return the same average but the quality of the data is very different and shown by the experimental uncertainty taken from the range of the data.

The uncertainty in our course is taken to 1 significant figure which gives the precision of the average, and is calculated by taking the range of results/2

this makes our two example averaged answers of 1.38 actually:

1.38 ± 0.01 (so the result should lie between 1.37 - 1.39)

1.4 ± 0.3 (so the result should lie between 1.1 - 1.7)

Now one experimental result is clearly better, even though the averages were the same. Note the second answer only has one decimal place as the uncertainty is given to a single significant figure and the number of decimal places in the uncertainty defines the number of decimal places the average should be given to (or more correctly the uncertainty defines the precision of the average).

Moving from raw data to processed data tables

I like a very clear approach to data collection. I want to see the raw data clearly presented in a report. Then later I want  to be told what the relevant uncertainties were and how the have been used in the data processing, followed by a processed data table. Other teachers suggest these can be combined but I would be against that and prefer to keep them separate.