research-design

To design an A/B experiment with guardrails, define your baseline and treatment, then establish primary metrics and stopping rules. This process generates a structured design doc outlining sample sizes, time windows, and event date rules for review.

Guardrails in A/B testing are predefined success metrics and stopping rules that prevent invalid conclusions. They include look-ahead bias checks to ensure statistical significance is evaluated correctly, protecting the experiment's validity throughout the research process.

You calculate sample size and duration by defining your baseline and treatment parameters within a structured design doc. The design workflow specifies time windows and event date rules to ensure your research-phase decisions gather sufficient data for statistical significance.

Yes, you can use this A/B design process for marketing and policy experiments. It applies to research-phase decisions across product, marketing, or policy contexts by setting baselines, treatments, samples, durations, and success criteria tailored to each specific domain.

The best way to document experimental design is generating a structured design doc. This output captures baseline versus treatment configurations, primary metrics, guardrails, and implementation paths, streamlining the transition from hypothesis to execution for review.

You should establish stopping rules during the initial A/B study design phase before implementation begins. Defining these guardrails alongside primary metrics and success criteria prevents premature experiment termination and ensures robust statistical significance evaluation.