Controlled Experiments & Confounders
Master experimental design for AI systems: randomized controlled trials, stratified allocation, blinding in LLM-as-a-judge evaluations, and neutralizing confounding bias and Simpson's Paradox.
Statistical Power & Factorial Designs
Master sample size determination, Minimum Detectable Effect (MDE), statistical power (1-beta), and 2^k factorial experimental design to isolate interaction effects in multi-component AI pipelines.
Cross-Validation & Reproducibility
Master cross-validation as an experimental design protocol, eliminate data leakage vectors, enforce pre-registration, and establish deterministic reproducibility across AI systems.