Before You Launch Your AB Test
Design better A/B tests to minimize insignificant results
Overview
A/B tests fail most often before they start — through unclear hypotheses, weak instrumentation, or misaligned success criteria. This course looks at how to design experiments that answer real product questions, define measurable outcomes, and avoid false positives. It emphasizes disciplined thinking about causality and risk so teams can invest in the right experiments. Best for PMs and growth practitioners running experimentation programs who want more reliable signals.
Instructors
Related Courses
These recommendations prioritize the same primary tag first, then broader tag overlap, then shared category context.
Analytics for Product Managers
Master analytics to make data-driven decisions
Customer Data Analytics with R
Learn critical skills to analyze customer data and derive actionable product insights
Data Visualization Nanodegree Program
Learn storytelling and dashboard design with Tableau
Data Product Manager Nanodegree Program
Gain expertise in data-driven product management
Data Science for PM
Apply data analysis skills to create compelling product proposals through real-world cases
Digital Product Management
Learn to build effective digital products with modern tools and metrics.