It is all easy and intuitive when the experimental unit is discrete, "a person", "a mouse" etc. However, say Peter has taken 5 soil samples from each experimental plot (which is the experimental unit), running a protocol of minuscule analytical measurement 5 times with the 5 samples, each time 0.1g of soil is used (n=5 at the plot level). Tom did it another way, he dug 5 samples and composited them together and run a larger (more wasteful) analytical protocol taking 10g of sample at once and only did the measurement once (n=1 at the plot level). Assuming both analytical protocol is equally valid, Peter as a whole analyzed 0.5g, but Tom analyzed 10g of soil! However, when you run statistical analysis and compare different plots, Tom's result would be less powerful because of the smaller sample size (number), right?
Surely, there has to be some way to "formalize" a continuous experimental unit, like every gram of soil per m2 of soil * 10cm deep is considered a sample size of 1. But it seems nobody has proposed anything close to it in soil science. Am I missing something? Did I mix up something between statistical power and representativeness? (which the latter is more subjective and can't be quantified)
Edit: Let's simplify this, say there are only two plots and we are gonna compare them by t-test. Peter has 5 measurements (each measurement used only 0.1g) from each plot and Tom has only 2 measurements per plot but he used a protocol which process more soil i.e. 10g per measurement (you cant even do a legit t-test if you have 1 measurement each plot). Also, note that a perfect homogenization of soil samples is very unlikely, with some soil properties even susceptible to change from homogenization efforts. Surely there are some logical problems when we take the conventional meaning of sample "size", no?