I am running some experiments in which I plot the values of some variable y (averaged over 4 runs) against some independent variable x. I also compare the effect of z. In other words:
- horizontal axis: x
- vertical axis: y
- plots: one line for each value of z
I would like to know if there are any best practices or rules of thumb to decide whether my error bars should reflect (a) min-max values, or (b) standard deviations. What characteristics of the data set should lead me to prefer one over the other?
Some things that I think might be relevant:
- The raw values of y are integers bounded between 0 and 60.
- I can only perform a small number (e.g. 4-5) replications due to the time cost of generating each raw data point.
- There can be some pretty wild fluctuations, e.g. y could be in the high 50's for 3 of the runs, but 20 in the last one.
- The data does not follow any model