Transform normal distribution to skewed distribution without changing its support I've found many questions and answers about transforming skewed distribution to normal. This question might arise because the simplicity of working with normal data. But, is there any function that transform normal to skewed data without changing the interval of the support? how can this be done?
 A: The support of a normal random variable is $(-\infty,\infty)$. Consider a normal variate, $X$. 
You could create a skewed distribution with the same support with a transformation, $Y=T(X)$ such that $T$ is bijective ($\mathbb{R}$ $\to \mathbb R$) and either convex or concave. The result of applying a convex bijective transformation will be right skew and have support $(-\infty,\infty)$ and a concave bijective ($\mathbb{R}$ $\to \mathbb R$) function will be left-skew and have support $(-\infty,\infty)$.
(Concave and convex functions are not the only way to get skew results, however.)
e.g. consider 
$T(x)=\begin{cases} x-1\,;& x \leq 1\\ \log(x)\,;& x > 1\end{cases}$
It will take a symmetric sample and squeeze in its values above 1, while leaving the lower values alone, resulting in a left-skew result (changing the parameters of the normal will alter how much impact this transformation has on the skewness).
[If, instead you want the sample range of the values to be the same after transformation, you can take a sample, transform it to the desired shape, and then use a linear transformation to match the minimum and maximum values.]
