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A CRO experiment brief reduces false wins by fixing the hypothesis, primary metric, sample plan, and stopping rule before anyone reviews results. It cannot eliminate false positives, but it makes it harder to mistake a lucky fluctuation or a selectively chosen metric for evidence that a change worked.
Why write the brief before launching a CRO test?
When teams choose success criteria after seeing a dashboard, chance variation and selective reporting can make an experiment look better than it is. Statsig advises defining in advance which metrics should move and keeping the confirmatory set small. Its documentation illustrates the risk with one statistically significant result among 20 metrics at a 5% significance level purely by chance; that is an illustration under the null, not a rule that one in every 20 observed wins is false. Read Statsig’s guidance on false positives.
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A useful brief is a decision plan, not just a description of a design change. It records what is being tested, for whom, how assignment works, which outcome answers the question, how much evidence the team needs, and what the team will do with the result.
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Complete the decision, measurement, and stopping fields before launching or looking at outcome data. Put the plan somewhere the owner and reviewers can see, and record any approved changes rather than silently revising the criteria.
Experiment identity and ownership
- Experiment name and ID:
- Owner and decision-maker:
- Reviewers and approval status:
- Planned launch date and review date:
Decision and hypothesis
- User or business problem:
- Proposed change: What differs from the control?
- Hypothesis: If [change] is shown to [audience], then [primary outcome] will [direction and expected size], because [behavioral mechanism or evidence].
- Disconfirming result: What result would count against the hypothesis?
Optimizely’s brief template includes experiment identity, owners, reviewers, hypothesis, details, metrics, and targeting. It defines a hypothesis as a prediction created before an experiment. See Optimizely’s experiment brief template.
Design and eligibility
- Control experience:
- Treatment experience:
- Design: State whether one factor changes or whether this is an explicitly planned multivariate design.
- Eligible audience and exclusions:
- Randomization unit: User, device, session, or another unit.
- Allocation ratio:
- Scope: Pages, platforms, geographies, and dates included.
- Concurrent changes: Campaigns or product changes that could affect the result.
Statsig describes experiments as randomized control and treatment variants and notes that assignment can be at the user, device, or session level. The chosen unit needs to be explicit so the team can interpret assignment and measurement consistently. See Statsig’s experiment interpretation guidance.
Metrics and guardrails
- Primary metric: Choose one preselected outcome that answers the decision question.
- Secondary metrics: Include only measures with a stated reason tied to the hypothesis. Mark other measures as exploratory.
- Guardrails: Identify outcomes that must not deteriorate materially, such as revenue quality, refunds, latency, or support burden when relevant.
- Metric definitions: Record event names, denominators, attribution window, and source of truth.
- Expected direction: State the expected movement and rationale for each confirmatory metric.
- Multiple comparisons: Record how you will handle them if there are multiple confirmatory metrics or variants.
Statsig cautions against examining too many metrics and selecting only favorable outcomes after results are visible. A short, hypothesis-linked confirmatory set limits opportunities for cherry-picking; exploratory measures can still be useful, but should not be presented as if they had been preselected to decide the test.
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Sample and analysis plan
- Baseline: Rate or mean, with the data period used to estimate it.
- Minimum detectable effect (MDE): The smallest effect worth detecting; express it in absolute and relative terms where useful.
- Significance target: The Type I error rate or alpha.
- Power target: The planned ability to detect the chosen effect if it is real.
- Allocation and sample: Ratio and planned sample per arm.
- Calculation method: Name the sample-size method and its assumptions.
- Analysis method: Fixed horizon, sequential, or another validated approach.
- Duration and stopping rule: State when the test ends and what conditions allow an early stop.
- Exposure window: Minimum time needed to include relevant weekly or business cycles.
- Decision rule: Define how positive, negative, and inconclusive results will be handled.
Statsig’s sample-size calculator identifies baseline conversion, MDE, split ratio, significance (alpha), and power as planning inputs. Alpha is the probability of detecting a significant difference when no actual difference exists. Use Statsig’s sample-size calculator. A sample target cannot be reduced to one visitor-count rule: it depends on the baseline, effect worth detecting, error-rate and power choices, allocation, outcome variability, and test design. Optimizely’s technical explanation discusses alpha, beta, and MDE in error control. Read Optimizely’s explanation of A/B test statistics.
Instrumentation and launch checks
- Verify assignment and exposure logging.
- Verify primary and guardrail events in both arms.
- Check audience rules and exclusions.
- Name the data-quality owner and monitoring plan.
- Define rollback or stop conditions for a broken implementation or material guardrail harm.
Readout and decision
- Report the preselected primary outcome with its estimated effect and uncertainty interval.
- Report guardrails and relevant secondary measures, including unfavorable results.
- Record the sample reached, dates, exclusions, and implementation issues.
- State whether the prewritten decision rule was met.
- Record the decision: ship, reject, extend only under the precommitted rule, or call the result inconclusive.
- Note the follow-up learning or experiment.
How to avoid false wins in a CRO test
Do not define success after seeing the dashboard
Write the hypothesis, primary metric, thresholds, sample plan, and stopping rule first. If several measures matter, say which ones are confirmatory and why; do not promote whichever metric happens to look favorable into the definition of success.
Do not treat a p-value as the probability the treatment works
A p-value is evaluated in relation to a null model; it is not the probability that the treatment is effective. Report the estimated effect and uncertainty interval alongside the test result so decision-makers can judge both direction and precision.
Keep the analysis and stopping rule stable
A fixed-horizon test and a sequential method designed for repeated monitoring do not have interchangeable stopping behavior. For a fixed-sample plan, do not repeatedly inspect ordinary significance results and stop at the first favorable read. If the team needs repeated reads, choose and document a sequential approach before launch.
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Fixed-horizon or sequential: choose the stopping behavior before launch
The right choice depends on the decision, traffic, acceptable false-positive and false-negative risk, and the team’s ability to follow the method. The available guidance supports planning inputs and the importance of method choice; it does not establish a neutral ranking of testing tools or statistical methods.
| Planning question | Fixed-horizon test | Sequential method |
|---|---|---|
| When do you stop? | At the preplanned sample or horizon; do not treat an early favorable ordinary read as the planned final result. | According to the method’s specified repeated-monitoring rule. |
| What must be decided before launch? | Baseline, MDE, significance, power, allocation, sample target, and stopping rule. | The same decision inputs plus the sequential method and its monitoring rule. |
| What should drive the choice? | Whether a fixed collection period fits the traffic and decision needs. | Whether valid repeated monitoring is operationally important and the team can follow the chosen method. |
What makes a sample target credible?
A credible target follows from the design rather than a generic minimum number of visitors. Estimate a baseline from an identified data period, choose the smallest effect worth acting on, set significance and power assumptions, specify allocation, and use a calculation method that matches the outcome and analysis. If traffic is too low to reach the target in a useful timeframe, the honest options include changing the design or decision, accepting that the test may remain inconclusive, or choosing a different way to learn—not lowering the threshold after seeing the data.
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