I have a very large dataset (with > 2 million simulated values). I want to compute standard error for this dataset. To do that, I divide the standard deviation by square root of number of observations. However, because of the large number of observations, the standard error is quite low. Is there a way to subsample instead and compute standard error?
closed as unclear what you're asking by Ben Bolker, kjetil b halvorsen, Peter Flom♦ Dec 29 '18 at 12:29
Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
Since you have a large sample (>2M), by the Strong Law of Large Numbers, the sample variance will converge to the population variance almost surely. See https://math.stackexchange.com/questions/243348/sample-variance-converge-almost-surely
The standard error is a good estimate of the population variance when you have small samples. The standard error is the variance of the sample mean in the sampling distribution.