foundations-validation

Assumption validation identifies your critical bets upfront, ranks them by risk, and defines kill criteria to stop wasting resources on unproven ideas. Map assumptions early, then design experiments with clear success metrics to test the riskiest ones first.

Experiment design for MVP validation requires defining testable hypotheses, success criteria, and measurable outcomes before launch. Structure experiments with clear kill criteria so you learn whether to pivot, persevere, or stop before heavy investment.

Risk-ranked assumption mapping identifies which bets pose the greatest threat to your product's success. Prioritize high-impact, high-uncertainty assumptions and test those before lower-risk bets to accelerate learning and reduce wasted effort.

Yes. Assumption mapping, experiment design, and MVP validation work across discovery, validation, and iterative learning phases. As you test and learn, update your assumption register and design new experiments based on results to continuously de-risk decisions.

Structured outputs include assumption registers that catalog your bets, kill criteria that define failure thresholds, success metrics that measure learning, and experiment portfolios that organize your test plan. These artifacts create accountability and track progress.