Why 80% of A/B tests fail, how to 10X your experimentation velocity - Kristi Angel - The Data Scientist Show #088
Daliana's Game
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Optimizing Experimentation Process for Effective Decision Making
The chapter emphasizes the significance of proper planning and alignment among partners for successful experimentation, stressing the need to identify target populations, define precise problem statements, and select relevant metrics. It discusses the importance of choosing meaningful metrics for A/B testing, focusing on decision-making, guardrail, and secondary metrics to guide experiment outcomes effectively. Additionally, it explores the ideal team structures for data-driven decision-making, highlighting the value of clear communication, support for data scientists, and knowledge sharing within teams.
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