I have a list of 20 experiments, each have a certain numerical value as a result. I would like to run many more experiments of this type (for different conditions, and so on), but 20 experiments are too expensive.
What's the best way to estimate the optimal number of experiments, for which additional experiments change the mean only slightly?
I have tried randomly sampling a subset (2,3,...18 and so on) of the experiments, calculating the standard deviation for each (for a number of iterations), trying to see from which number of experiments the std change only slightly, this is what I got after averaging all the STD per number of samples: (y-axis is the mean STD, x-axis is the number of experiment sampled from the total 20)
However, I'm not sure this is the optimal way to do such a thing.