This appendix refines Proposition 4.1 by improving statistical efficiency in hypothesis testing. It minimizes false positives and optimizes rejection regions for success and guardrail metrics, ensuring more reliable decision-making.
Spotify’s decision rule framework for A/B testing optimizes statistical accuracy and risk management by integrating superiority, non-inferiority, and quality tests.
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Extending A/B test decision rules with deterioration and quality metrics helps catch regressions and ensures experiment integrity. Spotify’s Decision Rule 2 formalizes this approach.
A/B testing decisions must balance Type I and Type II errors. This section explains UI/IU testing principles, Bonferroni corrections, and power adjustments for non-inferiority testing.
A/B testing relies on multiple metrics with distinct roles. Spotify’s framework categorizes them into success, guardrail, deterioration, and quality metrics, ensuring a standardized, risk-aware decision process.
A/B testing with multiple outcomes requires structured decision-making. Spotify draws insights from decision theory, OECs, and clinical trials to refine its approach, ensuring reliable, scalable experimentation.
Monte Carlo simulations test multi-metric A/B decision rules, revealing how alpha and power corrections affect error rates, ensuring reliability in statistical experiments.
A/B testing decisions rely on statistical error control. This section explores superiority and non-inferiority tests, Bonferroni adjustments, and how multiple-testing corrections shape experiment outcomes.
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