Data Driven Strategy and Experimentation Questions

Covers how to balance quantitative evidence and qualitative judgment when making product and technical decisions. Topics include recognizing limits of observational data, designing and interpreting split testing and cohort analysis, causal inference fundamentals, measurement frameworks and success metrics, statistical power and sample size considerations, making decisions with incomplete or noisy data, prioritizing experiments for strategic bets, building analytics and data science partnerships, teaching non data stakeholders statistical thinking, and cultivating an organizational culture of experimentation and learning where hypothesis driven work informs prioritization.