Experiment Reporting Template Checklist
A standard format for writing up A/B test results so stakeholders trust the call. Covers hypothesis, variants, segment breakdowns, recommendation, and documented learnings.
Experiment Reporting Template
A standard format for documenting A/B test results — hypothesis, variants, segment performance, recommendation, and learnings — so stakeholders trust the call.
An experiment reporting template is the fixed document structure your team uses to write up every test, win or lose. Each readout covers the same sections in the same order: the hypothesis you tested, what shipped in each variant, the headline result with confidence and lift, segment breakdowns, a clear ship/kill/iterate recommendation, and the learnings captured for the next round.
The template is less about the metric and more about the social contract. When every readout looks the same, finance stops re-litigating methodology, product stops cherry-picking favourable segments, and the experimentation programme compounds because past tests become readable artefacts instead of buried Slack threads.
Most experimentation programmes don't stall because the tests are bad. They stall because the readouts are inconsistent. One PM ships a 0.3% lift as a win, another kills a flat result without checking mobile, and by quarter three nobody trusts the numbers.
A reporting template solves that by forcing the same six questions on every test: what did you believe, what did you build, what happened, who did it happen to, what should we do, and what did we learn? You're not writing a story — you're filing a record that survives team changes and audit conversations.
The readout is part of the experiment
If you only write up the winners, your win rate is a fiction. Every test that reaches its sample size gets the same template, including the flats and the losses. That's where the compounding learning lives — and where stakeholders learn to trust the programme.
The six sections every readout needs
Section one is the hypothesis and context. State the original hypothesis verbatim — in the form 'we believe that [change] will cause [outcome] because [evidence]' — plus the primary metric, the guardrails, and the planned sample size. Quoting the pre-test hypothesis prevents the post-hoc rewriting that quietly turns a flat test into a 'directional learning'.
Section two is variants and exposure. Screenshot or describe control and each variant, traffic split, devices included, dates live, and total visitors per arm. If you ran on Shopify and excluded checkout traffic or paused for a Black Friday weekend, write it down here. Future-you will not remember.
Section three is the headline result, then segments. Lead with the primary metric: lift, confidence interval, p-value or Bayesian probability, and whether you hit your pre-registered significance threshold. Then break it out — by device, new vs returning, traffic source, and order-value tier. A flat overall result that wins +8% on mobile and loses -6% on desktop is a different decision than a flat result that's flat everywhere.
Sections four through six are the decision and the memory. Recommendation: ship, kill, or iterate, with one sentence of reasoning. Learnings: what you now believe about your customers that you didn't before, regardless of the result. Next test: the follow-up hypothesis this readout unlocks, so the programme always has a queue. File the document somewhere searchable, tag it by page and hypothesis type, and link it from your experimentation roadmap.
Frequently asked questions
One to two pages, or roughly 500-800 words plus screenshots. If it's longer, you're probably narrating the journey instead of filing the record. If it's shorter, you're likely skipping the segments or the learnings section.
Yes — that's the whole point. Inconsistent reporting between wins and losses is how programmes lose credibility. A loss documented in the same format as a win is what makes the next 'we should ship this' recommendation believable.
Whoever owned the hypothesis owns the readout. On smaller teams that's often the CRO specialist; on larger teams the PM writes the narrative and an analyst signs off on the stats. The owner's name goes on the document either way.
Three to five planned cuts is the sweet spot: device, new vs returning, and one or two business-relevant segments like traffic source or product category. Avoid open-ended slicing — the more cuts you try, the more likely you'll find a false positive.
Document it the same way and call it inconclusive in the recommendation. State the observed lift, the confidence interval, and the minimum detectable effect at the sample you collected. 'We can't tell' is a legitimate outcome — pretending you can is the problem.
Summary stats in the document, raw data linked. The readout is for decision-makers; the underlying export is for anyone who wants to re-run the analysis. Keeping both connected protects you from the 'where did this number come from' conversation six months later.
Lead with the guardrail. If conversion rate is up 4% but average order value dropped 7%, the headline is the net revenue impact, not the conversion lift. Burying a guardrail movement under a primary-metric win is the single fastest way to lose stakeholder trust.
Somewhere searchable by hypothesis, page, and result — Notion, Confluence, or a shared drive with consistent tagging. The point is that a new PM joining in six months can answer 'has anyone tested this before?' in under five minutes.
Monthly is a good cadence. Pull the last four to six readouts, look for patterns across hypotheses, and update your testing roadmap. This is where the programme stops being a series of tests and starts being a body of knowledge about your customers.
Same six sections, but expand the variants and results sections. Multivariate tests need a variant matrix; long-running holdouts need a timeline view of cumulative impact. The hypothesis, recommendation, and learnings sections stay identical.
Test ideas before you ship them
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