Course Introduction ​

Welcome to A/B Testing from Hypothesis to High-Impact. This course is the definitive guide for product managers, marketers, and designers who want to stop guessing and start knowing. In product development, the most expensive phrase is "I think." A/B testing is the superpower that replaces opinions with evidence, allowing you to make decisions based on the most reliable source of truth available: the behavior of your actual users.

This course demystifies the entire experimentation lifecycle. We will go far beyond simply comparing two versions of a button. You will learn how to formulate powerful hypotheses grounded in real data, design statistically valid experiments, and interpret the results with confidence. By mastering this skill, you will reduce the risk of launching features that fail, systematically optimize your user experience, and build a powerful engine for increasing engagement and retention. We will cover everything from foundational A/B tests to more advanced A/B/n and multivariate testing, giving you a complete toolkit to drive meaningful growth.

Prerequisites ​

This course is designed to be a practical, hands-on guide for anyone looking to leverage experimentation to build better products. To ensure you can hit the ground running, you should have:

• Foundational Digital Product Knowledge: You should be familiar with the basic components of a digital product, such as websites or mobile apps, and understand concepts like user flows, landing pages, and calls-to-action (CTAs). This course is for practitioners (Product Managers, Marketers, Designers) who want to test and validate their ideas on a live product.
• Basic Analytical Acumen: A general awareness of common business or marketing goals, such as "increasing sign-ups" or "improving user engagement," is helpful. While familiarity with terms like "conversion rate" is a plus, it is not required as all key concepts will be defined clearly.
• A Hypothesis-Driven Mindset: The most critical prerequisite is a curiosity to ask "why" and a willingness to challenge your own assumptions. A/B testing is about moving from "I think this will work" to "Let's test if this works." Come prepared to embrace experimentation, learn from failure, and let user behavior guide your decisions.
• No Advanced Technical Skills Required: You do not need to be a statistician or a developer to succeed in this course. We will cover the necessary statistical concepts, like "statistical significance," in simple, non-technical terms. The focus is on the strategy and process of experimentation, not on coding or complex mathematical formulas.

Course Objectives ​

Upon completing this course, you will be able to:

• Formulate High-Impact Hypotheses: Learn to generate clear, testable hypotheses based on quantitative evidence from analytics and qualitative insights from user feedback, ensuring every test is designed to produce valuable learning.
• Design Rigorous Experiments: Master the practical steps of designing a valid A/B test, including creating variants, defining the right target audience, and calculating the necessary sample size to achieve statistically significant results.
• Define and Measure Success: Select a single, decisive primary metric to determine the winner of a test, and identify critical secondary and guardrail metrics to monitor for unintended negative consequences.
• Interpret Results with Confidence: Understand the core statistical concepts behind A/B testing so you can confidently analyze outcomes, differentiate between a true winner and random chance, and avoid common analytical pitfalls.
• Communicate Learnings to Drive Action: Develop the ability to translate test results into compelling data stories that create alignment, turning every outcome—win, loss, or inconclusive—into actionable intelligence for your organization.