有放回与无放回抽样。

https://web.ma.utexas.edu/users/parker/sampling/repl.htm

1、有放回抽样，对于样本之间是独立的。数学上表示，有放回抽样的样本与原样本集合，协方差为0. 即

When we sample with replacement, the two sample values are independent. Practically, this means that what we get on the first one doesn’t affect what we get on the second. Mathematically, this means that the covariance between the two is zero.

无放回抽样，

In sampling without replacement, the two sample values aren’t independent. Practically, this means that what we got on the for the first one affects what we can get for the second one. Mathematically, this means that the covariance between the two isn’t zero.

In particular, if we have a SRS (simple random sample) without replacement, from a population with variance , then the covariance of two of the different sample values is , where N is the population size.

2、当样本总量足够大时，这两种抽样差别不大。

When we sample without replacement, and get a non-zero covariance, the covariance depends on the population size. If the population is very large, this covariance is very close to zero. In that case, sampling with replacement isn’t much different from sampling without replacement.

3、无放回抽样会导致 抽样出的样本 方差变小。这也是为什么方差为什么除以N-1。

“分母是n-1才能使对方差的估计（而不是方差）是无偏的”

https://blog.csdn.net/weixin_41776824/article/details/80548039