9 prompts

A/B Testing & Statistics

These are AB testing prompts for the full experimentation loop, from an idea sitting in a backlog to a shipped decision the business trusts. Use them as ChatGPT prompts for AB testing that turn testing from theater into evidence: prioritizing what to test next, writing a falsifiable test hypothesis, calculating sample size and how long to run a test, designing an AB test brief, QAing the setup with an SRM check before launch, analyzing winning and inconclusive results, choosing conversion rate vs revenue per user, and reporting results to stakeholders. As AI prompts for experimentation, they also help you stand up a program and build a testing culture that compounds learnings over time. Reach for them when you are a CRO lead, growth marketer, product manager, or analyst who needs a senior practitioner's operating procedure rather than gut feel, and who would rather kill a false win early than defend it later.

Research and diagnose

Measure Revenue per User, Not Just Conversion Rate

An A/B test metrics prompt that helps you choose conversion rate vs revenue per user, handle skewed variance right, and avoid false wins from AOV tradeoffs.

Build and launch

Calculate Sample Size and Test Duration

An A/B test sample size prompt that computes how long to run a test in whole weeks from your baseline conversion, effect to detect, and traffic.

Design an A/B Test That Will Not Lie to You

An A/B test design prompt that turns a fuzzy idea into a locked test brief with one primary KPI, stopping rules, and guards against peeking.

Prioritize an Experiment Backlog

A test prioritization prompt that scores and ranks your experiment backlog with PXL or ICE models so you know how to prioritize A/B tests by evidence.

Stand Up an Experimentation Program

An experimentation program prompt that builds the strategic case, picks an org structure, and lays out a roadmap for how to build a testing culture.

Write a Falsifiable Test Hypothesis

A ChatGPT prompt for an A/B test hypothesis that turns a research finding into a falsifiable if then because statement with mechanism and metric.

Optimize and test

QA an Experiment Before Launch

An experiment QA prompt that runs an A/B test QA checklist covering tracking, cross browser preview, flicker, and an SRM check before you go live.

Measure and report

Analyze a Test and Decide What Ships

An A/B test analysis prompt that reads winning, losing, or inconclusive results with segment checks and tells you what to do with an inconclusive test.

Report Test Results to Stakeholders

An A/B test report prompt that turns p values and confidence intervals into an experiment readout non technical stakeholders can act on.

Start here: Design an A/B Test That Will Not Lie to You for the single most load bearing skill in this cluster, then Analyze a Test and Decide What Ships to read the results without fooling yourself.

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