We've gone over this concept in my class a couple of times, and it still isn't clicking.
Could someone please prove this statement:
The ideal bootstrap standard error of the mean
$$se_\infty(\bar{x})$$
(The standard error of bootstrap resampled means, where the number of bootstrap samples $B=\infty$),
is equivalent to the usual estimated standard error of the mean: $$se(\bar{x}) = \dfrac{\sqrt{\dfrac{1}{n}\sum_{i=1}^n (x_i - \bar{x})^2}}{\sqrt{n}} = \dfrac{s}{\sqrt{n}}$$
Please be as detailed as possible.
Note: Here is an explanation given by my TA, I'd like it if you could clarify his explanation:
The "ideal bootstrap estimate of the standard error" is the standard error of a statistic under the distribution $F_n$ where $n$ samples are drawn iid from the empirical distribution. The variance of the mean of $n$ such variables is $1/n$ the variance of a single variable (using general properties of the mean). The variance of a single variable is by definition just the variance of the empirical distribution, i.e., the empirical variance. This is the desired claim.