An evidence level is a rating of how much confidence to place in an assumption, based on the strength of the evidence supporting it, from an untested assertion or rough estimate through to validated, real-world data. It turns "how sure are we?" into an explicit, comparable signal that guides where to gather more evidence before investing further.
Every strategy hypothesis rests on assumptions, and not all are equally well supported. An evidence level makes that support explicit: each assumption is rated for the strength of the evidence behind it, on a graduated scale from an unsupported guess to validated, real-world data. Paired with an assumption's impact, the evidence level is what tells a team which beliefs to test first, the high-impact, low-evidence assumptions, and lets investment scale with confidence rather than optimism. It is the common signal that connects modeling, experimentation, and the investment decision. Related Terminology Index entries: Strategy Choice Assumptions; Uncertainty Assumptions; Stage Gate.
The principle that new ventures should be run to convert assumptions into evidence comes from the lineage of Rita McGrath and Ian MacMillan's Discovery-Driven Planning (1995), Steve Blank's customer development, and Eric Ries's validated learning in The Lean Startup (2011); the discipline of scaling investment as evidence accumulates is the basis of Robert Cooper's Stage-Gate process. BRI's refinement is to attach an evidence level to every assumption across the strategy model, so confidence is tracked consistently rather than argued anecdotally. Growth Forge® Software puts this to work in the Assumptions Manager and Evidence Gathering tools, where evidence levels prioritize experiments and feed the Strategy Evaluation.
