Let’s say you have limited time and resources to conduct any experiment. Which would be the most effective way to determine whether or not your results indicate a true effect? Taking more smaller samples or taking fewer but larger samples?
Everything points to larger samples sizes being better for reducing variance, but nothing I can find compares size vs effort. Obviously assuming independent sampling, etc.
For example, to determine bug community composition in soil… Should one take many small soil samples, or a couple large volumes of soil?
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Seconding /u/Emyrssentry that this is going to be hugely variable depending on exactly what you’re researching.
What you’re describing here is two separate sources of variation, namely the variation within individuals and between individuals (though with soil the definition of individual becomes a bit more complicated — plots of land with clearly separate conditions?). You want your measurement to be true to the situation as it was in the whole individual, i.e. if I were testing for some biomarker in human blood I might need a minimum volume not just for the actual testing, but also to ensure I get a representative sample of that individual’s entire several-liter blood volume.
That’s all just optimizing your methodology, and statistics should generally come after that. But again, very much specific to your field.
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