How might we interpret quantitative findings in simulation research more meaningfully?

Quantitative findings carry particular authority in simulation research and are often read as decisive evidence that an intervention has—or has not—made a meaningful difference. Statistical significance can function as an interpretive shortcut, providing a ready-made storyline that compresses uncertainty and context. This debate article examines how such readings arise and why they risk exceeding what quantitative results produced in simulation research can legitimately be taken to support. The argument distinguishes what common statistical outputs are designed to show from what they are often asked to mean. A p-value describes how incompatible the observed data are with a no-difference model; it cannot by itself demonstrate learning or clarify how and why an observed contrast arose. In simulation research, this gap is often widened when samples are modest and when outcomes reflect behaviour that develops unevenly across clinical settings and over time. Under these conditions, interpretations that hinge on statistical significance alone become fragile. To make these limits explicit, the paper highlights four constraints on inference that clarify what a quantitative contrast can reasonably support: (1) uncertainty around the estimate, made visible through confidence intervals; (2) effect size understood in relation to variability and the conditions under which outcomes are produced; (3) the relevance of observed changes for clinical, organisational or educational work; and (4) the influence of systematic influences including information bias, selection bias, and confounding. A hypothetical emergency department resuscitation programme example illustrates how the same numerical contrast can support different conclusions once these constraints are taken seriously. Quantitative results remain central to simulation research, but they do not function as verdicts on whether an intervention worked. Their value depends on careful interpretation—treating statistical outputs as inputs to judgment rather than as decisive evidence. Making the constraints on inference explicit supports interpretations that remain proportionate to what the data can sustain and strengthens the development of cumulative knowledge in simulation research.