Designing a valid A/B test is more than statistics—it’s the foundation of confident financial decision-making. In this intermediate-level course, you’ll learn how to frame hypotheses and build A/B test plans that reveal whether new algorithms or strategies truly improve performance. You’ll start by mastering the logic of null and alternative hypotheses, then apply A/B testing principles to measure an algorithm’s impact on the Sharpe ratio, a key indicator of risk-adjusted return. Through short videos, practical readings, and hands-on labs, you’ll practice designing controlled experiments that separate genuine improvement from random noise. You’ll also learn how to document your test plan clearly for quant-lead review and interpret outcomes with statistical rigor. By the end, you’ll be able to design, explain, and evaluate experiments that turn data into financial insight—skills essential for analysts, quantitative researchers, and finance professionals who test ideas before they scale.