  The paper proposes one-to-one transformation of the vector of components
$\{Y_{in}\}_{i=1}^m$ of Pearson's chi-square statistic,
\[Y_{in}=\frac{\nu_{in}-np_i}{\sqrt{np_i}},\qquad i=1,\ldots,m,\] into another
vector $\{Z_{in}\}_{i=1}^m$, which, therefore, contains the same "statistical
information," but is asymptotically distribution free. Hence any
functional/test statistic based on $\{Z_{in}\}_{i=1}^m$ is also asymptotically
distribution free. Natural examples of such test statistics are traditional
goodness-of-fit statistics from partial sums $\sum_{I\leq k}Z_{in}$. The
supplement shows how the approach works in the problem of independent interest:
the goodness-of-fit testing of power-law distribution with the Zipf law and the
Karlin-Rouault law as particular alternatives.
