Practical methods for efficient adaptive experiments and bandit-style testing that converge faster on better options while preserving trustworthy inference.
Practical guidance to decide when adaptive or bandit-style experiments make sense, how to design them safely, what inputs you must specify, how to monitor and guard inference, and a simulation checklist and protocol sections to include so teams can run adaptive experiments confidently.
A practical, method-forward guide to when and how to run adaptive experiments and bandit-style tests. Explains core algorithms, trade-offs versus fixed-sample A/B tests, instrumentation and stopping rules that protect trustworthy inference, fairness and operational constraints, and a compact decision checklist plus a suggested simulation workflow to test choices before you run live.
A practical guide to decide when to use sequential or bandit-style adaptive experiments, how to design them responsibly, what telemetry and stopping rules you need, common trade-offs versus fixed-sample A/B tests, and a concise feasibility checklist plus a worked example that shows potential efficiency gains and risks.