Launch Effective A/B Tests

Coursera connects the design of an online A/B test with the statistical and business decisions that follow it. Tracking, performance uplift and practical significance help explain why a result can be statistically interesting without being worth implementing. It suits learners who already know basic statistics and want to connect that foundation with an operational experiment.

What you will learn

• Design and launch an online A/B test.
• Review tracking and statistical interpretation.
• Compare practical significance with rollout criteria.

Who this suits

Intermediate learners with a background in basic statistics and data-analysis tools, the preparation recommended by the provider. The course is short and focused on experiment decisions.

Before you enroll

A conversion change needs evidence, a useful outcome measure and enough context to interpret it. Case-study gains are not expected results. Check the current version and cost of analytics, testing or page-building tools; older interface examples may need adaptation. Use fictional data for practice, document uncertainty and avoid misleading visitors when applying persuasion or design ideas.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Create a fictional test brief with an outcome measure and rollout criterion. Explain how you would interpret an uncertain result and identify a case where a small measured improvement would not justify the implementation cost.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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