control group
2 stories filed under control group on Beyond Market Intelligence. The newest of them: “See Through the Hype of Your AI Feature's Adoption Numbers” and “Optimize A/B tests by allocating traffic based on variant cost, not tradition”. When a team opts into an AI feature, the lift you measure isn't just the tool's effect. The default 50/50 traffic split assumes equal cost per variant. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every control group story on Beyond Market Intelligence, newest first.
See Through the Hype of Your AI Feature's Adoption Numbers
When a team opts into an AI feature, the lift you measure isn't just the tool's effect. It's a selection effect, a reflection of who chose to engage. This guide offers a practical framework for estimating what that opt-in feature actually did, even without randomization. It's a sharp, honest look at a messy problem. For practitioners ready to dig deeper into the mechanics of modern AI systems, our guide to distributed algorithms pairs well with this mindset.
Optimize A/B tests by allocating traffic based on variant cost, not tradition
The default 50/50 traffic split assumes equal cost per variant. That assumption breaks when your treatment costs more than your control. This post tackles the math behind optimal allocation under heterogeneous variant cost, showing how cost-based sampling weights correct the imbalance. It's a practical fix for anyone running experiments where budget constraints matter. For readers interested in how similar efficiency principles apply beyond experimentation, our piece on the Forrester function explores a related mathematical tool for machine learning.