Experimentation System Design

Most A/B Tests Are Wrong

Teams peek at results early, declare winners on underpowered tests, use the wrong metrics, and ignore confounding variables. The result? Confident decisions based on statistical noise.

At Zapier, I helped build the experimentation culture that drove massive growth. I design systems that produce results you can actually trust — with proper power analysis, sequential testing, and metric hierarchies that align experiments with business outcomes.

Common Pitfalls I Fix

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  Peeking: Checking results daily and stopping early inflates false positive rates to 30%+.
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  Low power: Running tests that can't detect realistic effect sizes wastes weeks and produces null results.
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  Poor metrics: Optimizing for clicks when revenue is what matters leads teams in the wrong direction.
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  Confounding: Not accounting for seasonality, cohort effects, or network interference invalidates results.

Experimentation Framework

End-to-end system design: randomization, assignment, analysis pipeline, and decision rules.

Experiment Design Guide

Templates for power analysis, metric selection, and pre-registration your team can reuse.

Statistical Models

Production-ready analysis code with sequential testing, CUPED variance reduction, and Bayesian options.

Implementation Plan

Step-by-step engineering spec for integrating the framework into your product and data stack.