Experiment Documentation
The practice of recording experiment hypotheses, designs, results, and learnings in a structured format — creating an organizational knowledge base that compounds over time.
Experiment documentation is the least glamorous and most valuable part of an experimentation program. Without it, every insight dies in a Slack thread, every new team member starts from zero, and the organization runs the same failed tests repeatedly.
What to Document
Every experiment record should include: (1) the hypothesis in structured format ("If we [change], then [metric] will [direction] because [behavioral reason]"), (2) the target metric and guardrail metrics, (3) sample size calculation and expected runtime, (4) results with confidence intervals, and (5) learnings — what did we learn about our customers, regardless of whether the test "won"?
The Learning Repository
The most valuable part of documentation isn't the results — it's the learnings. "Loss aversion framing doesn't work for our audience in acquisition contexts" is worth more than "Test #47: variant B lost by 3%." Learnings transfer across tests and contexts. Results don't.
Why Hiring Managers Care
In interviews, I ask candidates to walk me through an experiment they documented. The best candidates can explain not just what they tested and what happened, but what they learned and how it influenced future tests. This demonstrates the compounding mindset that separates program builders from test operators.
Practical Application
Use a structured template for every experiment. Store it somewhere searchable (Notion, Confluence, or a dedicated experimentation platform). Tag experiments by page, audience segment, behavioral principle, and outcome. Review the repository quarterly to identify patterns and generate new hypotheses from old learnings.