Experimental Design and A/B Testing

Build rigorous, testable hypotheses for A/B experiments. Turn business questions into statistically sound, prioritized test frameworks ready for execution.

Interpret A/B test results accurately and confidently. Understand p-values, confidence intervals, effect sizes, and practical significance to make correct ship decisions.

Apply Bayesian inference to A/B experiments for more intuitive, flexible results. Calculate posterior probabilities, expected loss, and credible intervals for better decisions.

Design valid causal studies when randomized A/B testing isn't possible. Apply difference-in-differences, regression discontinuity, instrumental variables, and synthetic control methods.

Define and validate audience segments for targeted A/B experiments. Improve test sensitivity and relevance by identifying the right users for each experiment.

Choose, configure, and scale your A/B testing platform strategically. Compare Optimizely, LaunchDarkly, Statsig, Eppo, and custom solutions for your team's needs.

Design and analyze multivariate experiments beyond simple A/B splits. Master full factorial, fractional factorial, and Taguchi designs for complex product optimization.

Detect and mitigate threats to A/B test validity including novelty effects, Hawthorne bias, SRM, carryover effects, and instrumentation errors before they corrupt results.

Calculate the right sample size for A/B tests with precision. Avoid underpowered experiments and false positives using statistically sound power analysis guidance.

Implement valid early stopping rules for A/B tests without inflating false positive rates. Master sequential probability ratio tests, alpha spending, and always-valid inference.