A/B testing and experimentation

Wiki

• Topic

  A/B testing

• Topic

  Statistical power

• Topic

  Sample size

• Topic

  Confidence interval

• Topic

  P-value

• Topic

  Minimum viable test

Playbooks

• Playbook

  Run growth experiments

  Create a structured experimentation system that prioritises high-impact tests, produces valid results, and builds institutional knowledge with every cycle.

Articles

• Article

  Analysing and acting on results

  Statistical significance is just the beginning. Learn how to interpret results correctly, avoid false positives, and turn winning experiments into permanent improvements across your growth engines.

• Article

  Creating strong hypotheses

  Most experiments fail before they start because the hypothesis is vague or untestable. Learn how to write hypotheses that are specific enough to prove or disprove and tied to metrics that matter.

• Article

  Turning insights into action

  A folder full of interview notes is worthless if nothing changes. Learn how to spot patterns across conversations and turn what you heard into better copy, sharper ads, and stronger sales conversations.

• Article

  Setting up experiments

  A winning test means nothing if the setup was flawed. Learn how to configure experiments properly in VWO, ad platforms, and email tools so your results are actually valid.

• Article

  Building your backlog

  Random testing wastes time and teaches you nothing. Learn how to collect experiment ideas systematically and prioritise them based on potential impact so you always know what to run next.

• Article

  Compound learnings across experiments

  Build a knowledge base from past experiments so new tests build on proven insights instead of starting from scratch every time.