# How Box cox and other transformations convert data into Normal Distributions?

How Box cox and other transformations convert data into Normal Distributions ?

Transformations happen by finding the estimate of $\lambda$ and some values in the neighborhood are chosen to transform the original data.
$$y(\lambda) = \left\{ \begin{array}{ll} \frac{y^{\lambda} -1 }\lambda \quad if \lambda \neq 0; \\ log(y), \quad if \lambda = 0. \end{array} \right.$$
Which basically turns into choosing $\lambda$ as a model parameter. This value is found by maximizing log-likelihood.