Suppose $X_1$ is a standard normal variable. Define, $$X_2=\begin{cases}-X_1, & \text{if } |X_1|<1,\\X_1, & \text{otherwise}\end{cases}$$
Show that $X_2$ is also a standard normal random variable.
My approach:
Let,$$F(X_2)=P[X_2\leq x_2]\\=P[X_2\leq x_2\mid|X_1|\leq 1].P[|X_1|< 1]+P[X_2\leq x_2\mid|X_1|\geq 1].P[|X_1|\geq 1]\\=P[-X_1\leq x_2].P[|X_1|<1]+P[X_1<x_2].P[|X_1|>1]$$
But I cannot compute the probabilities.May be,my approach is not right.
You have the correct idea so far, but are missing a key trick. Recall that if $X_1$ is a standard normal distribution, then $X_1$ is symmetric around 0. This means that
$$P(X_1 < x_2) = P(X_1 > -x_2) = P(-X_1 < x_2) = P(-X_1 \leq x_2). $$
Using this, the result will follow directly from your last step.
I would divide it into 3 cases:
$X_2 \le 1$
$-1 \lt X_2 \le 1$
$1 < X_2$
The first is trivial, since $X_1$ is standard normal. For the second,
$ \begin{align} F(x_2) &= F(-1) + P(-1 \lt X_2 \le x_2) \\ &= F(-1) + P(-x_2 \le X_1 \lt 1) \\ &= F(-1) + P(-1 \lt X_1 \le x_2) ~by~symmetry \end{align} $
which is standard normal since $X_1$ is.
The third case is similar.
