  {\em Ziggurat} and {\em Monty Python} are two fast and elegant methods
proposed by Marsaglia and Tsang to transform uniform random variables to random
variables with normal, exponential and other common probability distributions.
While the proposed methods are theoretically correct, we show that there are
various design flaws in the uniform pseudo random number generators (PRNG's) of
their published implementations for both the normal and Gamma distributions
\cite{Ziggurat,{Gamma},Monty}. These flaws lead to non-uniformity of the
resulting pseudo-random numbers and consequently to noticeable deviations of
their outputs from the required distributions. In addition, we show that the
underlying uniform PRNG of the published implementation of Matlab's
\texttt{randn}, which is also based on the Ziggurat method, is not uniformly
distributed with correlations between consecutive pairs. Also, we show that the
simple linear initialization of the registers in matlab's \texttt{randn} may
lead to non-trivial correlations between output sequences initialized with
different (related or even random unrelated) seeds. These, in turn, may lead to
erroneous results for stochastic simulations.
