Those doing the analysis grasp where it is uncertain and what that means for how far results can be trusted, and always pass this on to decision makers. Sources include system variability, unreliable or insufficient data, ambiguous qualitative terms, models that do not capture complexity, heavy reliance on expert judgement, missing data, out-of-date past data and uncertain assumptions. Where reliable data is lacking, more is collected if practicable or the process adjusted. Sensitivity analysis shows which inputs matter, sensitive parameters and degrees are stated, and critical changeable parameters are monitored so the assessment can be updated.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.