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?
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.