Building a Facebook Ads hypothesis testing framework for scaling campaigns separates high-performing teams from those stuck in optimization plateaus. Many advertisers run ad tests reactively, without clear hypotheses or measurement criteria, resulting in inconclusive data and delayed growth. This guide establishes a structured methodology: define what you expect to learn, set sample size requirements before the test concludes, document winning variants, and feed learnings into subsequent campaign phases. The framework addresses common pitfalls such as multiple comparisons errors, premature scaling on insufficient data, and creative fatigue patterns that distort test results. By applying these principles systematically across accounts and verticals, media buyers can compress testing cycles from weeks to days and achieve compound improvements in efficiency metrics that compound into multi-month ROI gains.