Sample size (number of samples) vs the size of your samples (sample effort?)

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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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Anonymous 0 Comments

It is dependent on the type of study being done. One of the professors in my field of research (functional MRI scanning) is currently quantifying that single subject scan time is most important up to ~30 minutes, then it becomes more important to add more subjects at that 30 minute time frame.

I don’t know much about other areas, but it seems like something that is highly variable based on the specifics of the research.

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