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Module
Module 5 of 5Lesson 2 of 2~22 min

Final workshop: your experiment's protocol, analysis and memo

The final workshop applies the approach to your own product, from the experiment protocol through to the decision memo. If volume falls short, a reasoned alternative plan takes the place of the test, and a rubric helps you judge how solid the whole package is before you share it with engineers and the data team. You leave with a reusable kit and a plan to launch your first test.

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Lesson objective

By the end of this lesson, you will have written the protocol of an experiment on your product (or a reasoned alternative plan), analyzed a real result or the practice dataset provided, written the decision memo, self-assessed the whole with a grid, and you will leave with an exit kit and a 7-day and 30-day plan.

Questions this lesson answers

  • How do you write a complete A/B test protocol step by step?
  • What checklist should you follow before and after launching an A/B test?
  • What AI prompts can help review an A/B test protocol or memo?

Topics covered

  • A/B testing workshop
  • experiment protocol
  • decision memo
  • self-assessment rubric
  • experimentation kit

Where it fits

Decide and capitalize

How do I turn a result into a decision, and the test into lasting learning?

Lessons in this module

  1. Decide by written rules and document the experiment
  2. Final workshop: your experiment's protocol, analysis and memo (this lesson)

What you will learn in this module

This lesson is part of the course Design and analyze an A/B test

  • Decide whether an A/B test is the right method (volume, B2B, reversibility, time, ethics) and choose a suitable alternative otherwise.
  • Write a testable hypothesis, choose a sensitive, attributable primary metric, guardrails and the exposure event to log.
  • Compute a sample size and a duration from the baseline rate, the minimum detectable effect, the significance level and the power.
  • Analyze a result (p-value, confidence interval, practical significance, guardrails) and read a tool's Bayesian or frequentist output.
  • Decide based on rules written before the test and document the experiment in a reusable memo.

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