Plan product experiments from a testable hypothesis through execution, diagnostic checks, and a measured decision.
Develop a practical experiment workflow that connects a product question to a clearly defined treatment, comparison group, and outcome. Learners consider random assignment, exposure and measurement, practical significance, and the risks of reading too much into a noisy result. The original activities use fictional or appropriately authorized aggregate data; they emphasize predeclared decisions, guardrails, and honest uncertainty rather than promising that any test will produce a definitive answer.
Translate a product question into a falsifiable hypothesis, treatment contrast, target population, and primary metric. Draft an experiment plan that addresses assignment, exposure, duration, guardrail metrics, and foreseeable interference. Interpret a test readout with uncertainty and practical impact, and distinguish evidence from a decision recommendation.
Familiarity with product metrics, basic percentages, and introductory statistical ideas such as averages and sampling is recommended. Assignments can be completed on paper with invented data. Do not run experiments on people without appropriate organizational approval, privacy review, and safeguards; use aggregate or synthetic examples and never make high-impact decisions from a tutorial exercise.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
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