Ab-Testing
A/B Testing Triggers: Counterfactual Logging Makes ML Algorithm Experiments Super Efficient!
Ab-Testing
Deep dive into three A/B testing trigger methods: User-Level Triggering, Exposure Logging, and Counterfactual Logging. Learn from real industry cases at DoorDash and Spotify to solve experiment dilution problems effectively.
A/B Testing Triggers: Separating Signal from Noise in User Experiments
Ab-Testing
Introducing A/B testing trigger mechanisms that shows how precision targeting of affected users can cut sample size requirements in half. Essential reading for understanding why lots of experiments fail due to diluted effects and how to design more powerful tests.
Your A/B Test is Significant, But Is It Real? Understanding False Positive Risk
Ab-Testing
P-value < 0.05 ≠ You have 95% chance of being right
Why A/B Test Matters: The Science Trumps Gut Feelings
Ab-Testing
Causal-Inference
Product-Analytics
The limitations of human intuition and the power of controlled experiments in modern product development.