A practical, hands-on guide for reducing uncertainty around new products and business models by turning assumptions into testable hypotheses and running rapid, low-cost experiments; it explains how to surface and prioritize the riskiest assumptions (desirability, feasibility, viability), choose the right experiment type (from smoke tests and landing pages to prototypes, concierge tests, interviews and A/B trials), define success metrics and learning goals, iterate based on results, and use templates and an experiment library to help cross-functional teams learn quickly and decide whether to persevere, pivot, or kill an idea before investing heavily in development.