What is a reasonable sample size for discovering public opinion or conducting neuroscience? In the US, 1000 seems a bit small for a population of over 300 million.

Really not my area of expertise, but I feel like it will depend greatly on 1) how well can you randomize you sample 2) how strong the effect that you're looking for is 3) how confident you want to be.



I don't think 1000 people is necessarily an unusably small sample even for a population as large as 300 million as long as you're sampling it really randomly.

>>15407731 (OP)

Sample size of 1 is usually enough in neuroscience.

>>15407731 (OP)

It completely depends on a lot of factors (including the assumed output model). 



1000 might be considered a small amount, but is it truly? It depends on the factors and how much you think they matter and how much randomization. I.e., people from New York have different opinions than people from Florida but is this general opinion a GREATER factor than rural and urban? Are you going to include all the factors you can think of? 



Basically, it's not that a 1000 is small for 300 million (the size of the population actually doesn't matter all that much). A much more important input is all the different factors associated with individual sentiment/biology that's what drives sample sizes up along with Power (a.k.a. how well you expect the sample size to give reasonable results given all the factors you're considering and the outputting model form). Basically, the more ways you can think to cut up a population determines your sample size way more than population. Also how much you want to capture variability matters a lot as well. It's why you'll occasionally see studies with really small samples. It's not because their not doing due diligence, it's typically more so because it's a pilot study and they care less about capturing variability and more about seeing if there might be something to study in greater detail (with more money) later. 



Anyway, this is all really the backbone of modern statistics and the entire field is called Design of Experiments (and there are A LOT of different flavors focused on surveying, biology, industrial manufacturing, computer experiments/models). It's also a big factor in lying with statistics.

>>15407731 (OP)

you jack off to child pornography

If you want more information, I think there's actually a fairly good low-level Coursera course ran by the guy who wrote the modern text on general DoE and then the other flavors are typically only talked for their particular subfields. Like you don't see anyone talk about design and analysis of computer experiments unless their working with very computationally expensive computer models. That's also a big factor in all this. What resources do you have? 



It's all basically a big 'space-spreading' problem. For all these problems, you want to effectively explore the space of all the relevant covariants while minimizing resource consumption and effectively randomizing to 'cut' the effects of confounding variates (i.e., not measured or hidden).

>>15407731 (OP)

The entire group. The data is there somewhere anyways. If you want to account for missing data then you have to know what's missing and why in the first place.

You're in like half the threads talking about child pornography. What's wrong with you?
