Sequential Hypothesis Testing: Making Data Decisions Over Time

Traditional A/B testing requires waiting for a fixed sample size, which costs valuable time. Sequential hypothesis testing allows you to analyze data as it arrives, enabling faster decisions without sacrificing statistical rigor. In this text-only course, you will learn how to design and analyze sequential tests, transitioning from static methods to dynamic decision-making frameworks. What you will learn: Understand foundational concepts of hypothesis testing and why fixed-sample methods fail in continuous streams; Master the Sequential Probability Ratio Test (SPRT) to make real-time decisions; Apply early stopping rules safely without inflating error rates; Configure dynamic sample sizes for product analytics; Analyze the trade-offs between decision speed and statistical accuracy. You will start with core statistical definitions before progressing to step-by-step implementation scenarios. This course is designed for beginner data scientists and product analysts, with no advanced prerequisites required. Start reading today to make faster, smarter data-driven decisions.