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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTo run a credible A/B test, turn a product decision into a falsifiable hypothesis, randomly assign eligible units to stable experiences, choose outcomes and guardrails before launch, and plan the sample and analysis in advance. Then validate assignment and logging before interpreting results, report effect sizes with uncertainty, and decide against practical launch criteria—not a p-value alone.
1. Start with the product decision and hypothesis
Make the question testable
Begin with a decision the team might actually make. A useful hypothesis names a change, the expected direction of an outcome, and the population it applies to. For example: “Moving the sign-up form to the center of the page will increase the sign-up rate among eligible visitors.” This is an illustrative hypothesis, not a claim about an observed result.
Define the control as the current experience and specify exactly what changes in the treatment. If the treatment bundles several changes, a result may not tell you which one caused the difference. Keep experiences stable for each assigned unit throughout the test so the comparison reflects the planned variants.
Define success and unacceptable harm
Choose one primary metric that directly tests the hypothesis. Write down its numerator, denominator, eligible population, and measurement window; “conversion” is not precise enough unless everyone agrees what counts as a conversion and who is at risk of converting. Select secondary metrics to help explain the result, and guardrails for outcomes the team does not want to worsen, such as reliability, latency, or a broader business outcome.
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Set decision criteria before seeing results. Specify the smallest effect worth acting on, any guardrail limits, and what you will do if the result is positive, inconclusive, or harmful. Statistical evidence and practical value are different: a small estimated improvement can be statistically detectable yet not worth shipping, while a potentially valuable estimate may remain too uncertain to justify a decision.
2. Choose the randomization unit and define the data
Match assignment to the treatment’s reach
Randomize eligible units, rather than letting people choose a variant. Assignment creates the basis for a causal comparison. The right unit is the smallest unit that can receive treatment without meaningful spillover or contamination. If a feature affects an entire organization, assigning individual users in that organization to different arms may expose both experiences and undermine the comparison.
| Assignment unit | Consider it when | Watch for |
|---|---|---|
| User | The feature affects individuals and users do not materially influence one another. | Shared accounts, cross-device identity, or users seeing both variants. |
| Account or organization | The experience or outcome is shared across members of a team or customer account. | Fewer independent units and possible differences in account size or activity. |
| Another treatment-relevant unit | The intervention acts on a different entity, such as a session or another defined entity. | Whether repeated observations or interactions violate independence assumptions. |
Randomly allocate eligible units according to the planned ratio. An uneven split can limit exposure to a risky change, but the sample plan must use that allocation; do not assume a 50/50 plan when the test is not balanced. Never assign a systematically different group—such as “power users”—to one arm by design and interpret the result as if assignment were randomized.
Separate eligibility, assignment, exposure, and outcome
Record these as distinct concepts. Eligibility determines who may enter; assignment records the randomized arm; exposure records whether the unit actually received or encountered the variant; outcome events record what happened afterward. A unit can be assigned without being exposed. Defining the comparison population using post-assignment behavior can change which units are compared, so state the analysis population and denominator explicitly.
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Check that assignment persists, no unit is inadvertently exposed to both variants, and both arms emit comparable events. Instrumentation differences can look like product effects if one variant causes events to be logged or lost differently.
3. Plan sample size and test duration
Choose inputs before calculating
A conventional power calculation needs the baseline rate or outcome variance, the minimum detectable effect (MDE), the tolerated Type I error rate (alpha), desired power, and allocation ratio. The MDE should represent the smallest effect that would change the decision, not simply the effect that makes a sample-size estimate convenient. Smaller effects and higher desired power generally require more observations. Proportion metrics such as conversion need a baseline rate; continuous metrics such as time spent or payment amount need an estimate of variance.
Statsig’s 2021 sample-size article describes alpha = 0.05 and power = 0.8 as common planning settings. They are conventions, not universal requirements. The article’s derivation also assumes equal standard deviations under the null and MDE for small effects, so a calculation is only as suitable as its metric assumptions. For highly skewed outcomes such as duration or revenue-like measures, consult a method appropriate to the metric and randomization design rather than treating a simple estimate as definitive.
When more than one primary metric is decision-critical, estimate the sample need for each and plan for the longest required duration, as Statsig’s design guidance recommends. If allocation is unequal, use a method that accounts for the intended split.
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Translate the sample into a calendar plan
Estimate duration from the required number of eligible units and the traffic expected to qualify during enrollment. Then account for enrollment patterns and operational cycles, including weekday and weekend behavior when relevant. There is no universal calendar duration established by these planning principles: a fixed “two-week” rule is not a substitute for a sample and enrollment estimate.
Before launch, record the planned sample, allocation, expected enrollment pace, and the stopping rule. If traffic is lower than expected, extend only in accordance with the plan; do not stop simply because an early result looks favorable.
4. Validate experiment health before interpreting lift
Check assignment counts and sample ratio mismatch
Compare the observed counts with the planned allocation among the relevant assigned or exposed population, and verify which count the platform or analysis is using. A sample ratio mismatch (SRM) means the observed group counts differ materially from the intended split. It is a warning that eligibility, assignment, exposure, or data processing may have failed—not a nuisance to repair with reweighting before finding the cause.
Thresholds vary by source and implementation. Statsig said its product used p < 0.01 as an SRM warning threshold in its 2023 diagnostic guidance. A 2023 technical primer in the Encyclopedia of Machine Learning and Data Science gives p < 0.001 as an example of a very low SRM p-value that should trigger a strong warning and suppression of scorecards. These are source-specific examples, not interchangeable universal cutoffs. A material mismatch warrants investigation even if it does not cross a chosen threshold.
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Trace likely causes
- Confirm both arms use the same eligibility rules and that assignment occurs at the intended point in the flow.
- Check whether exposure is logged consistently and whether assigned-but-unexposed units are being excluded unexpectedly.
- Review randomization code, identity resolution, and the possibility that a unit receives both variants.
- Look for differential crashes or other failures that prevent one arm from reaching exposure or emitting outcome events.
- Inspect data pipelines for arm-specific record deletion, duplication, or processing delays.
Do not interpret an SRM-affected scorecard as a trustworthy treatment estimate until the discrepancy is understood and the analysis population is defensible.
Run the other trust checks
Check instrumentation parity, test power, latency and performance differences, and interactions with overlapping experiments. When only a defined subset could have been affected, triggered-user analysis may improve sensitivity if the triggering definition is appropriate and planned. Pre-experiment covariates such as CUPED can also improve sensitivity in suitable designs; they do not repair faulty assignment or logging.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Analyze results without overstating certainty
Report the estimate and its uncertainty
For the primary outcome, report the treatment-control difference in absolute terms and, when useful, relative terms. Include an uncertainty interval, the number of randomized and exposed units, and the exact analysis population. State the analysis method and standard-error approach appropriate to the metric and randomization unit; repeated observations or organization-level assignment may require analysis that reflects that structure.
A p-value is not the probability that the treatment works. Interpret it alongside the estimated effect, interval, assumptions, planned sample, and practical threshold. If the interval includes both a meaningful benefit and meaningful harm, the result is uncertain even if one point estimate looks appealing.
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Keep confirmatory and exploratory comparisons distinct
Keep the preselected primary outcome separate from secondary and exploratory metrics. Searching across many outcomes, variants, or segments increases the chance that at least one apparent win is a false positive. Statsig’s September 2026 article discusses family-wise error risk and approaches including Bonferroni and Benjamini–Hochberg. Choose a correction suited to the family of hypotheses and decision, and disclose it. A post hoc segment can generate a follow-up hypothesis, but should not quietly replace the primary result.
Follow the monitoring plan
For a fixed-horizon test, repeated checking of the primary result and stopping when it looks favorable can inflate false-positive risk. Use sequential monitoring only when it was selected and planned in advance. Routine guardrail monitoring for obvious breakage is a separate operational safeguard; it is not permission to repeatedly search primary outcomes for a win.
6. Decide and communicate the result
Apply the criteria you set before launch
Compare the effect estimate and interval with the practical ship threshold, then assess guardrails and broader trade-offs. A local metric improvement may not justify a change if it damages a more important user or business outcome. If launch criteria are not met, do not describe statistical significance on a secondary metric as a reason to ship the treatment anyway.
Possible decisions include shipping, not shipping, or gathering more evidence. More data is appropriate only if the remaining uncertainty matters to the decision and the test can continue without changing the planned design or analysis in a way that invalidates inference.
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Use a readout that another analyst can audit
- Product question, hypothesis, control, and treatment.
- Assignment unit, allocation ratio, eligibility rules, dates, and analysis population.
- Primary metric definition, secondary metrics, guardrails, MDE, planned sample, power, and stopping rule.
- Assignment, exposure, instrumentation, and SRM checks, including any unresolved issues.
- Analysis method, uncertainty intervals, and multiplicity handling.
- Effect estimates, practical interpretation, decision, and limitations.
This makes clear not only what happened, but how much confidence the team should place in the conclusion and what decision the evidence supports.
Quick Recap
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