A/B Testing & Experimentation Design

I design and analyze controlled experiments that help you make confident, data-backed decisions. Whether the goal is to optimize marketing campaigns, improve conversion rates, refine product features, or validate operational changes, I ensure each experiment is statistically sound, properly powered, and free from bias.

I structure tests from the ground up by defining clear hypotheses, determining appropriate sample sizes, selecting the right experiment type (A/B, multivariate, split-path, holdout), and establishing the statistical framework that governs how results are interpreted.

My approach ensures:

• Accurate detection of meaningful differences

• Avoidance of false positives/negatives

• Clean experiment execution with minimal contamination

• Valid conclusions that can be trusted for business decision-making

What I Deliver

1. Experiment Design Protocols

• Clear, testable hypotheses aligned to your business goals

• Selection of the correct experiment type (A/B, MVT, sequential tests)

• Sample size and statistical power calculations

• Definition of primary and secondary KPIs

• Randomization strategy and audience allocation

• Duration estimates based on traffic, variance, and expected lift

• Pre-registration documentation to prevent p-hacking and bias

2. Test Execution Framework

• Data collection plans to ensure clean, structured inputs

• Event instrumentation guidance (tracking, tagging, logging)

• Guardrail metrics to monitor unintended impacts

• Interim analysis rules (or guidance to avoid premature stopping)

• Optional: platform configuration support (Google Optimize alternatives, Meta Ads experiments, CRM testing systems, etc.)

3. Rigorous Statistical Analysis

• Hypothesis testing using the appropriate method (t-tests, z-tests, non-parametric tests, Bayesian inference, sequential analysis)

• Confidence intervals and lift calculations

• Conversion funnel analysis to understand where variants differ

• Statistical significance, effect size, and power re-evaluation

• Checks for bias, novelty effects, sample ratio mismatch (SRM), and instrumentation noise

• Data-quality diagnostics to ensure results aren’t skewed

4. Validated Recommendations & Insights

You receive a decision-ready, comprehensive report including:

• Summary of experiment goals and setup

• Performance comparisons between variants

• Statistical test results and confidence metrics

• Interpretation of findings (not just “which variant won,” but why)

• Practical recommendations for rollout, redesign, or retesting

• Optional impact modeling to forecast long-term business effects