Stimulated by the problem Let $Z\sim N(0,1)$ be a random variable, then $E[\max\{Z,0\}]$ is? I came up with this problem:
Let $x_i, i=1..n$ be $n$ independent random variables $\sim N(0,1)$.
1) Caculate the PDF of the minimum and maximum of the $x_i$, respectively, for $n = 2$ and $n = 3$
2) Calculate the first and second moments
3) generalize the results to any $n$
Here's my start. Let $x$ and $y$ be the random variables. The PDF of $x$ is
$$f(x) = \frac{e^{-\frac{x^2}{2}}}{\sqrt{2 \pi }}$$
and similarly for $y$.
For the easy of writing we combine $min$ and $max$ into $ext$
Let
$$t=ext (x,y)$$
The PDF of $t$ is given by
$$fExt(t)=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }f(x) f(y) \delta (t-ext (x,y))dydx$$
I solved the problem for n = 2, and partly for n = 3. In order not to spoil your fun I shall provide my solution depending on your answers.
I have found for the PDFs the following expressions.
for the minimum
$$f_{min}(n,t)=\frac{n}{\sqrt{2 \pi }} \exp \left(-\frac{t^2}{2}\right) \left(\frac{1}{2} \left(1-\text{erf}\left(\frac{t}{\sqrt{2}}\right)\right)\right)^{n-1}$$
for the maximum
$$f_{max}(n,t)=\frac{n}{\sqrt{2 \pi }} \exp \left(-\frac{t^2}{2}\right) \left(\frac{1}{2} \left(1+\text{erf}\left(\frac{t}{\sqrt{2}}\right)\right)\right)^{n-1}$$
Reference to earlier work
For the pdf of the minimum see the answer of wolfies to https://stats.stackexchange.com/questions/77692/expected-value-of-minimum-order-statistic-from-a-normal-sample
For the maximum case there are related references which, however, give the expected value rather than the PDF
Expected value for maximum of n normal random variable
Expectation of the maximum of gaussian random variables