Sampling from a standard normal distribution: CLT, Box-Muller and rnorm.

Following Exercise 2.3. from Introducing Monte Carlo Methods with R by Robert & Casella.

Given
$$ U_1, U_2, \dots, U_{12} \overset{iid}{\sim} \mathbb{U}[-\tfrac{1}{2}, \tfrac{1}{2}], $$ set $$ Z = \sum_{i=1}^{12}{U_i}. $$
Then, $$ \mathbb{E}[Z] = \mathbb{E}[\sum_{i=1}^{12}{U_i}] = \sum_{i=1}^{12}{\mathbb{E}[U_i]} = \sum_{i=1}^{12}{0} = 0, $$ and $$ \mathbb{V}[Z] = \mathbb{V}[\sum_{i=1}^{12}{U_i}] = \sum_{i=1}^{12}{\mathbb{V}[U_i]} = \sum_{i=1}^{12}{\tfrac{1}{12}} = 1, $$ where $\mathbb{V}[\sum_{i=1}^{12}{U_i}] = \sum_{i=1}^{12}{\mathbb{V}[U_i]}$ because $U_1, U_2, \dots, U_{12}$ are $iid$.

We sample $n = 10^4$ observations in three different ways: appealing to CLT (see above), using the Box-Muller algorithm and finally calling R's base rnorm.

A quick look reveals all the samples show the visual characteristics of a standard normal distribution: bell shape, symmetry, centered at $0$. We focus on the tails now.

The central values are well aligned, meaning algorithms produce similarly distributed samples to the center, but the tails of the observations from the Box-Muller algorithm differ from CLT and rnorm.

From KDE, we can see two things. First, the CLT approximation is wider between $[-2, \tfrac{1}{2}]$ and $[\tfrac{1}{2}, 2]$, yet it doesn't offer a good approximation at the peak. Second, rnorm provides fatter tails compared to the other two algorithms.

Still, I think it's worth coming up with additional ways to compare the samples, especially to characterise the tails. Any idea on how to do that?