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A CRO experiment brief helps prevent false wins by fixing the hypothesis, key metrics, sample plan, and stopping rule before results appear. It cannot eliminate false positives, but it can make cherry-picking and opportunistic stopping much harder. Use the template below to decide what you will test, how you will measure it, and what result will justify action.
What should an experiment brief include?
Record the experiment’s ownership, hypothesis, design, metrics, sample and analysis plan, instrumentation checks, and decision rule. The brief is useful only if the team agrees to those choices before looking at outcome data.
Experiment identity and ownership
- Experiment name and ID
- Owner and decision-maker
- Reviewers and approval status
- Planned launch and review dates
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].”
- What result would disconfirm the hypothesis?
Write the prediction before launch. A hypothesis should connect the proposed change to an expected metric movement and explain why that movement is plausible. Optimizely’s published experiment brief template includes fields for the experiment, owners, reviewers, hypothesis, details, metrics, and targeting: Optimizely experiment brief template.
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Describe who can enter the test, what each group experiences, and how assignment works. These details help the team interpret results and detect implementation problems that could compromise the comparison.
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Design and eligibility
- Control experience and treatment experience
- Whether the test changes one factor or uses an explicitly multivariate design
- Eligible audience and exclusions
- Randomization unit: user, device, session, or another defined unit
- Allocation ratio
- Pages, platforms, geographies, and dates in scope
- Known concurrent campaigns or changes
Statsig describes experiments as randomized control and treatment variants, with assignment possible at the user, device, or session level: Statsig experiment overview. The randomization unit matters: if you assign by user, for example, the brief should make clear how repeat visits are handled.
Instrumentation and launch checks
- Verify assignment and exposure logging.
- Verify that primary and guardrail events are captured in both arms.
- Check audience rules and exclusions.
- Name the person responsible for data quality and monitoring.
- Define stop or rollback conditions for a broken implementation or material guardrail harm.
These checks are operational safeguards: a planned sample cannot rescue a test whose assignment or outcome events are recorded incorrectly.
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Which metrics should an A/B test track?
Choose one primary metric that answers the decision question. Include only secondary metrics with a stated connection to the hypothesis, and label metrics intended for exploration as exploratory. Select guardrails for outcomes that must not deteriorate materially, such as revenue quality, refunds, latency, or support burden when relevant to the business.
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- Secondary metrics: supporting measures with a reason tied to the hypothesis.
- Guardrails: measures that could reveal unacceptable harm.
- Definitions: event names, denominators, attribution window, and source of truth.
- Expected direction: what movement is anticipated for each confirmatory metric, and why.
- Multiple comparisons: how the analysis will handle multiple confirmatory variants or metrics.
Statsig warns that examining too many metrics increases exposure to false positives and that selecting only favorable outcomes after the fact invites cherry-picking. Its documentation illustrates the risk this way: at a 5% significance level, one statistically significant result among twenty metrics can arise from random chance under the null. That is an illustration, not a guarantee that one in twenty observed wins is false in every experiment. Statsig does not state a publication date for this guidance: Statsig guidance on interpreting experiment results.
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How do you plan sample size and duration?
Set the sample-planning assumptions before launch. There is no universal visitor-count threshold that makes every CRO test reliable: the needed sample depends on the baseline, the smallest effect worth detecting, error-rate targets, allocation, outcome variability, and design.
- Baseline: the existing rate or mean, plus the data period used to estimate it.
- Minimum detectable effect (MDE): the smallest effect that would matter for the decision; give absolute and relative terms where useful.
- Significance level: the target Type I error rate.
- Power: the chosen chance of detecting the specified effect if it exists.
- Allocation and sample: the split ratio and planned sample per arm.
- Calculation: the sample-size method and its assumptions.
- Duration: the expected test period and minimum exposure needed to cover relevant weekly or business cycles.
- Decision rule: how to classify positive, negative, and inconclusive results.
Statsig’s sample-size calculator identifies baseline conversion, MDE, split ratio, significance (alpha), and power as planning inputs. It defines alpha in relation to detecting a significant difference when no actual difference exists: Statsig sample-size calculator. A calculation method must match the test design and assumptions; Optimizely’s technical article discusses alpha, beta, and MDE in error control: Optimizely article on calculating sample size for A/B tests.
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Can you stop an A/B test when the result becomes significant?
Only if the analysis method was designed for the way you are monitoring the test. A fixed-sample test and a sequential method do not have interchangeable stopping behavior. For a fixed-horizon test, write down the sample target and stopping rule, then avoid repeatedly checking ordinary results and declaring a win at the first favorable read. If the team needs repeated interim reads, specify a validated sequential approach before launch.
Do not quietly lower the significance threshold mid-test. Optimizely says lowering the threshold can shorten the time needed to declare significance while increasing false-positive risk, and changing the setting can affect active tests. Keep the analysis rule stable, or explicitly use a method designed for sequential monitoring: Optimizely guidance on statistical significance.
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How should the team report results?
Use the readout to show whether the prewritten decision rule was met, not to invent a new definition of success. Report the estimate and its uncertainty, along with unfavorable findings and any implementation issues.
- Report the primary outcome with its effect estimate and uncertainty interval.
- Report guardrails and relevant secondary measures, including unfavorable results.
- Record sample reached, test dates, exclusions, and implementation issues.
- State whether the planned decision rule was met.
- Choose a decision: ship, reject, extend only when the precommitted rule permits it, or call the result inconclusive.
- Record follow-up learning or the next experiment.
A p-value is not the probability that the treatment works. Statsig presents it in the context of a null model and also discusses confidence intervals; report effect size and uncertainty rather than reducing the outcome to a binary winner label: Statsig guidance on interpreting experiment results.
Quick Recap
Copyable CRO experiment brief template
EXPERIMENT IDENTITY AND OWNERSHIP
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:
If [change] is shown to [audience], then [primary outcome] will [direction and expected size], because [mechanism or evidence].
What result would disconfirm the hypothesis?
DESIGN AND ELIGIBILITY
Control:
Treatment:
Single factor or multivariate design:
Eligible audience and exclusions:
Randomization unit:
Allocation ratio:
Pages, platforms, geographies, and dates:
Known concurrent campaigns or changes:
METRICS AND GUARDRAILS
Primary metric:
Secondary metrics and rationale:
Exploratory metrics:
Guardrails:
Metric definitions, events, denominators, attribution window, and source of truth:
Expected direction and rationale:
Multiple-comparison handling:
SAMPLE AND ANALYSIS PLAN
Baseline and estimation period:
MDE (absolute and relative, where useful):
Significance / Type I error target:
Power target:
Allocation and planned sample per arm:
Sample-size method and assumptions:
Analysis method: fixed horizon, sequential, or other validated approach:
Planned duration and stopping rule:
Minimum exposure window:
Decision rule, including inconclusive results:
INSTRUMENTATION AND LAUNCH CHECKS
Assignment and exposure logging verified:
Primary and guardrail events verified in both arms:
Audience rules and exclusions checked:
Data-quality owner and monitoring plan:
Rollback or stop conditions:
READOUT AND DECISION
Primary outcome estimate and uncertainty interval:
Guardrails and relevant secondary results:
Sample reached, dates, exclusions, and implementation issues:
Did the prewritten decision rule pass?
Decision: ship, reject, permitted extension, or inconclusive:
Follow-up learning or experiment:Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
