  The PAWN index is gaining traction among the modelling community as a
sensitivity measure. However, the robustness to its design parameters has not
yet been scrutinized: the size ($N$) and sampling ($\varepsilon$) of the model
output, the number of conditioning intervals ($n$) or the summary statistic
($\theta$). Here we fill this gap by running a sensitivity analysis of a
PAWN-based sensitivity analysis. We compare the results with the design
uncertainties of the Sobol' total-order index ($S_{Ti}^*$). Unlike in
$S_{Ti}^*$, the design uncertainties in PAWN create non-negligible chances of
producing biased results when ranking or screening inputs. The dependence of
PAWN upon ($N,n,\varepsilon, \theta$) is difficult to tame, as these parameters
interact with one another. Even in an ideal setting in which the optimum choice
for ($N,n,\varepsilon, \theta$) is known in advance, PAWN might not allow to
distinguish an influential, non-additive model input from a truly
non-influential model input.
