Business experimentation requires a strategic selection process based on three critical questions: what type of hypothesis you're testing (desirability, feasibility, or viability), how much evidence you already have, and your time constraints until the next decision point or funding deadline. When uncertainty is high, start with quick, inexpensive experiments to point you in the right direction. Gradually invest in more costly tests that produce stronger evidence as your knowledge increases. Four practical guidelines can improve the experiment selection: begin with cheap and fast approaches in early stages; strengthen evidence by running multiple experiments for each hypothesis; always choose experiments offering the strongest evidence within your constraints; and minimize uncertainty before building anything. Having visual frameworks that map experiments by time/cost requirements and evidence strength can help identify the most appropriate tests for your current situation. Different business types benefit from specific experiment sequences. For example, [[business-to-business|B2B]] software companies might start with [[stakeholder interview|stakeholder interviews]] before moving to [[clickable prototype|clickable prototypes]] and presales, while [[business-to-consumer]] hardware companies might create [[explainer video|explainer videos]], and then develop [[rapid prototype|rapid prototypes]] before [[crowdfunding]]. These sequences demonstrate how properly connected experiments build momentum and create increasingly compelling evidence over time. The experimentation journey naturally progresses from discovery to validation. [[Designing Experiments for Discovery]] provide initial insights and determine if your general direction has promise, while [[Validation experiments|validation experiments]] confirm these insights with stronger evidence once a direction is established. This progression reflects how business ideas evolve from concepts with high uncertainty to refined offerings backed by substantial proof. Following this structured approach allows you to systematically reduce risk while preserving resources. Next: [[Designing Experiments for Discovery]] Back to: [[Testing Business Model Hypotheses]]