  The distribution of the spin directions of galaxies has been a question in
the past decade, with numerous Earth-based and space-based experiments showing
that the distribution is not necessarily random. These experiments were based
on different statistical methods, one of them was a simple and empirically
verified open source $\chi^2$ method. Patel & Desmond (2024) proposed that
previous experiments showing non-random distribution are flawed since they
assume Gaussian distribution. To address that, they apply a new complex ad-hoc
statistical method to several datasets, none of them except for one were used
in the past to claim for a dipole axis. The new method showed that all datasets
except for one exhibit isotropy. This paper discusses the soundness of the
contention that Gaussian distribution cannot be assumed for galaxy spin
directions. More importantly, simple empirical analyses show that the new
statistical method is not fully responsive to asymmetry in the distribution of
galaxy spin directions, and does not identify non-random distribution even in
situations where a dipole axis clearly exists in the data, or when an
artificial bias is added to the data to create an extremely non-random dataset.
Results using Monte Carlo simulation show substantial differences between the
results of the simulation and the results of the new statistical method. Code
and data to reproduce the experiments are available, and released in a manner
that is easily reproducible. Possible reasons leading to the results are also
discussed. The claims that the actual results are different from the results
reported in the papers are examined in an open and transparent manner. These
claims are found to be inaccurate, as the previous literature results are fully
reproducible. These findings further reinforce the need to study astrophysical
or cosmological explanations for the non-random distribution.
