  The classical Central Limit Theorem (CLT) states that for a sequence of
independent and identically distributed (i.i.d) random variables with finite
mean and variance, the normalized sample mean converges to the standard normal
distribution.
  In $1946$, Victor Kac proved a Central Limit type theorem for a sequence of
random variables that were not independent. The random variables under
consideration were obtained from the angle-doubling map. The idea behind Kac's
proof was to show that although the random variables under consideration were
not independent, they were what he calls \textit{statistically independent} (in
modern terminology, this concept is called long range independence). The final
conclusion of his paper was that the sample averages of the random variables,
suitably normalized converges to the standard normal distribution.
  In the 1970's, Charles Stein revolutionized the field of probability by
discovering a new method to obtain the limiting distribution for a sequence of
random variables. Among other things, his method gave an alternative proof of
the classical Central Limit Theorem.
  We obtain an improvement of Victor Kac's result by applying Stein's method.
We show that the normalized sample averages converge to the standard normal
distribution in the Wasserstein metric, which is stronger than the convergence
in distribution.
