A/B Testing & Experimentation Platforms
Know what works — with statistics you can defend.
The problem
Teams ship changes and argue about whether they worked. Underpowered tests, peeking at results, and ignored sample-ratio mismatches lead to confident decisions built on noise.
What you get
A trustworthy experimentation setup with correct power analysis, guardrail metrics, variance reduction, and clear readouts — so wins are real, losses are caught early, and the team learns faster.
What's included
- Experiment design, power analysis, and metric definition
- Assignment and exposure logging with sample-ratio checks
- Bayesian and frequentist analysis pipelines
- CUPED and other variance-reduction techniques
- Guardrail metrics and sequential / always-valid testing
- Causal inference for cases where A/B testing isn't possible
Typical stack
PythonSQLBayesian methodsCUPEDCausal MLGrowthBookStatsig
Frequently asked questions
Bayesian or frequentist A/B testing — which is better?
Both are valid; the right choice depends on your decision cadence and risk tolerance. Bayesian readouts are intuitive for continuous decision-making, while frequentist tests fit fixed-horizon experiments. Engagements pick the framework that matches how your team actually decides.
What is CUPED and why does it matter?
CUPED (Controlled-experiment Using Pre-Existing Data) is a variance-reduction technique that uses pre-experiment data to shrink noise. It can meaningfully shorten the time needed to reach significance, letting you run more experiments per quarter.
What if we can't run a clean A/B test?
When randomization isn't possible, causal inference methods such as difference-in-differences, synthetic control, or regression discontinuity can estimate impact from observational data with stated assumptions and caveats.
Ready to get started with a/b testing & experimentation?
Tell me about your project and I'll come back with ideas, a clear scope, and next steps — usually within 24 hours. Free discovery call, no commitment.