Experimentation Strategy and Advanced Designs Questions

When and how to use advanced experimental methods and how to prioritize experiments to maximize learning and business impact. Candidates should understand factorial and multivariate designs interaction effects blocking and stratification sequential testing and adaptive designs and the trade offs between running many factors at once versus sequential A and B tests in terms of speed power and interpretability. The topic includes Bayesian and frequentist analysis choices techniques for detecting heterogeneous treatment effects and methods to control for multiple comparisons. At the strategy level candidates should be able to estimate expected impact effort confidence and reach for proposed experiments apply prioritization frameworks to select experiments and reason about parallelization limits resource constraints tooling and monitoring. Candidates should also be able to communicate complex experimental results recommend staged follow ups and design experiments to answer higher order questions about interactions and heterogeneity.