What VWO Gives an Experimentation Team—and What It Cannot Decide
A source-backed guide to VWO and Wingify statistical models, stopping approaches, approvals, health checks, team workflow, and program fit.
Articles exploring governance through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
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A source-backed guide to VWO and Wingify statistical models, stopping approaches, approvals, health checks, team workflow, and program fit.
Inside Booking.com experimentation: decentralized ownership, a central platform team, power and runtime controls, CUPED, and a quality-first KPI.
How Apple Product Page Optimization uses empirical-Bayes shrinkage, sequential evidence, credible intervals, and human decisions—and what it cannot prove.
What Google has publicly documented about experiment infrastructure, review, power, A/A calibration, Bayesian Conversion Lift, and decision-making.
A source-backed analysis of Netflix experimentation: its hub-and-spoke team, test workflow, statistical methods, decision rights, and company fit.
Why earlier AI waves stalled, what terminal agents changed, and how verified closed loops may reshape builders, work, and the companies we create.
Most people accept an AI agent’s first answer. A three-layer audit — primary-source check, realistic-input test, goal re-derivation — catches what pattern-matching misses.
Karpathy's 2023 LLM talk, rebuilt for 2026 — what changed in scaling, tool use, and security, and what founders deploying AI agents need to know.
Statistical failures compound into credibility damage. The Statistical Trust Deficit framework explains why rigor in SRM detection and false positive…
How Atticus Li governed 100+ annual tests across five NRG Energy retail brands, with explicit rules for stopping, collisions, prioritization, and evidence.