Let $\{X_i\}_{i=1}^n$ be an i.i.d. sequence of $\mathcal{N}(0, \sigma^2)$ variables, and consider the random variable $$Z_n : = \max_{i=1,\ldots,n}|X_i|.$$
I need to prove the bound
$$ E[Z_n] \leq \sqrt{2\sigma^{2}\log{n}} + \frac{4 \sigma}{\sqrt{2\log{n}}} \quad \text{for all } n \geq 2. $$
I know how to prove the bound $ E[Z_n] \leq \sqrt{2\sigma^{2}\log{n}} $ (using the moment generating function), which is even better, but the hint in the exrcise says that I should use the tail bound $$ P[|U| \geq x] \leq \sqrt\frac{2}{\pi}\frac{1}{x} e^{-\tfrac{x^2}{2}}, \quad \text{where $U$ is a standart normal r.v.} \qquad (1) $$ My idea. Since $Z_n$ is a non-negative r.v., then $$ E[Z_n] = \int_{0}^{\infty} P[Z_n \geq x] \ dx = \int_{0}^{\infty} \Bigl( 1 - \bigl(1 - P[|X_1| \geq x] \bigr)^n \Bigr) dx. $$ I tried to use the tail bound (1), but it was unsuccessful. I even don't understand why this integral converges.
I would appreciate any ideas. Thanks!