Experimentation Tests

Experiment Platforms — Advanced

Sequential testing, interference, stratified bucketing, bot filtering, portfolio holdouts, cross-platform migration, and governance at scale on experimentation infrastructure.

Experiment Platforms — Intermediate

Layers and holdouts, targeting and ramps, sticky assignment, sample ratio checks, warehouse exports, and operational hygiene on experimentation platforms. Mix of single-answer and multiple-answer items.

Experiment Platforms — Beginner

How A/B and feature-flag platforms assign users, log exposures, allocate traffic, and support safe rollout basics. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Advanced

Advanced power for experimentation programs: simulation-based power, cluster and geo designs, multiplicity, sequential and optional stopping, variance reduction, and aligning analysis models with power assumptions. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Intermediate

Planning and interpreting power for product experiments: effect sizes, variance, two-proportion and mean tests, one- vs two-sided tests, and minimum detectable effects. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Power Analysis — Beginner

Foundations of statistical power: Type I/II errors, significance level, sample size intuition, and why underpowered experiments miss real effects. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Causal Inference Basics — Advanced

Identification vs estimation, SUTVA, heterogeneous effects, mediation, double robustness, staggered DiD cautions, and design-first causal reasoning for complex product and policy settings. Mix of single-answer and multiple-answer items.

Causal Inference Basics — Intermediate

DAGs and backdoor adjustment, regression pitfalls, diff-in-diff intuition, instrumental variables, matching, and threats to validity in quasi-experiments. Mix of single-answer and multiple-answer items.

Causal Inference Basics — Beginner

Foundations of cause and effect: correlation vs causation, confounding, randomization, counterfactuals, and common observational pitfalls. Mix of single-answer and multiple-answer items.