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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> Which would be the most effective way to determine whether or not your results indicate a true effect?
Conducting an appropriate statistical analysis. It may be possible to conclude that you have almost certainly found a real effect without taking any more data. Or it may be possible to conclude that an infeasible amount of data would be required to settle the issue.
> Taking more smaller samples or taking fewer but larger samples?
> Obviously assuming independent sampling, etc.
I’m not sure I understand what point you’re making. If everything is random and independent, then taking “fewer but larger samples” won’t make any difference if the overall sample size stays the same. This distinction is only important if you expect the data to have some kind of hierarchical structure. e.g. you might expect that there will be a difference between taking 50 blood samples from one person and taking 1 blood sample from 50 people. But this depends strongly on the setting and on what you’re trying to achieve.
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