  Statistics experiences a storm around the perceived misuse and possible abuse
of its methods in the context of the so-called reproducibility crisis. The
methods and styles of quantification practiced in mathematical modelling rarely
make it to the headlines, though modelling practitioners writing in
disciplinary journals flag a host of problems in the field. Technical, cultural
and ethical dimensions are simultaneously at play in the current predicaments
of both statistics and mathematical modelling. Since mathematical modelling is
not a discipline like statistics, its shortcomings risk remaining untreated
longer. We suggest that the tools of statistics and its disciplinary
organisation might offer a remedial contribution to mathematical modelling,
standardising methodologies and disseminating good practices. Statistics could
provide scientists and engineers from all disciplines with a point of anchorage
for sound modelling work. This is a vast and long-term undertaking. A step in
the proposed direction is offered here by focusing on the use of statistical
tools for quality assurance of mathematical models. By way of illustration,
techniques for uncertainty quantification, sensitivity analysis and sensitivity
auditing are suggested for incorporation in statistical syllabuses and
practices.

Methodology