Experiment Lab: Validate Your A/B Tests with AI

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How many weeks have you run an A/B test only to end up with inconclusive results? Most tests fail before they start because no one checked if there was enough traffic. Today I show you how AI tells you in minutes whether your test will work, before you run it.

In this video you'll learn:
How to evaluate if an A/B test is viable BEFORE running it (power analysis in 2 min)
How to analyze results beyond the aggregate number
How to detect Simpson's Paradox: when the total lies and the segments tell a different story
The difference between GO, NO-GO, and PARTIAL ROLLOUT

Framework: Experiment Lab BEFORE and AFTER
BEFORE: Experiment Simulator (power analysis, viability, alternatives)
AFTER: Results Autopsy (SRM, segmentation, Simpson's Paradox)
DECISION: based on segments, not aggregates

2 live demos:
Pre-test: AI evaluates viability and says "don't run this test" with clear alternatives
Post-test: we upload a CSV and discover that a positive result hides two opposite stories

Article with full prompt and framework: https://open.substack.com/pub/product...

Timestamps:
0:00 The test that never had a chance
1:00 Why most A/B tests fail
2:30 Framework: BEFORE and AFTER
5:00 Demo 1: Experiment Simulator (pre-test)
7:00 Demo 2: Results Autopsy (Simpson's Paradox)
10:00 Summary and next steps

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Course: AI Product Management - Module 4: Optimization

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