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Why the numbers can differ
An experiment result is only meaningful alongside its population and measurement rules. A platform may report people assigned to a variant, people exposed to it, or people who completed a qualifying event. Analytics may count a narrower set: users whose events were recorded and who meet the report’s filters and attribution rules.
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Firebase documents this distinction directly: eligible users may fetch experiment parameters before they trigger the activation event that limits measurement. Assignment, exposure, activation, and conversion therefore need not describe the same group. See Firebase’s explanation of A/B test concepts.
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Reporting surfaces add another layer. Google documents that sampling, supported fields, filters, segmentation, modeling, date range, and processing delays can affect values across reports, Explorations, the API, and BigQuery. A difference between those views does not, by itself, identify which figure is appropriate for your question. See Google’s reporting data expectations and its guidance on differences between reports and explorations.
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Check what each metric actually counts
Matching labels such as “conversion rate” do not prove that two tools use the same calculation. One report might count users who converted; another might count conversion events, including repeat events. Revenue might mean total revenue or revenue per user. Firebase’s experiment results guidance distinguishes totals, metric-specific rates, and lift; check the definition behind each displayed value in Firebase’s results documentation.
- Unit: Is the row about users, sessions, devices or installations, or events? Check identity stitching and deduplication, too.
- Population: Does the denominator include everyone assigned, only people exposed to the variant, or only users who triggered an activation event?
- Outcome: Are the event name, conversion criteria, attribution window, currency, and handling of repeat events identical?
- Reporting rules: Are the dates, time zone, filters, segments, and dimensions the same, and does either view indicate sampling or delayed processing?
- Uncertainty: Are you comparing the same statistical method and interpreting intervals the same way?
Firebase’s guidance documents a significance threshold of 0.05 and 95% confidence intervals for its experiment results. Those are product-specific settings or examples, not universal standards for every A/B testing tool. A lift estimate and a confidence interval also answer different questions: the estimate describes the observed difference, while the interval communicates uncertainty under the method used.
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Reconcile the experiment from assignment to outcome
Work from the earliest observable step forward. Comparing final conversion rates before checking assignment counts can hide a broken or uneven measurement path.
- Choose one unit of analysis. Decide whether the comparison is at user, session, device or installation, or event level. Establish how each system identifies and deduplicates that unit.
- Define eligibility and allocation. Record who could enter the test and the expected split between variants. Keep assigned users distinct from users who actually saw or activated the changed experience.
- Align identifiers. Confirm that the experiment ID and variant identifiers are carried consistently into Analytics. Google’s third-party integration guide describes using an
experience_impressionevent and a variant parameter; its approach requires Analytics events to add users to a variant. See Google’s experiment integration guide. - Check event timing. Make sure the activation or exposure point occurs after the experiment parameters are fetched and before the changed experience can affect behavior. Firebase calls out this sequencing because recording activation too early or too late can change who is measured.
- Match the outcome rules. Verify the conversion event, eligibility criteria, attribution window, currency, and whether repeat events count once or multiple times.
- Match the reports. Use the same date range and time zone, filters, segments, and dimensions. Check for sampling metadata and allow for processing delays before comparing totals.
- Compare counts before rates. Inspect assignment counts by variant and event-level records before comparing conversion rates or drawing a statistical conclusion. Firebase notes that experiment and variant membership can be inspected on Analytics events in BigQuery, which can support an independent analysis.
- Investigate persistent gaps. Check client- or server-side logging failures, consent effects, duplicate events, cross-device identity, audience latency, and assignment implementation. Do not silently select the dashboard with the more favorable result.
Decide which result to use for which job
Google describes the third-party tool as the place to run and manage tests, with Analytics available to interpret test results after integration. That division makes the experiment platform a natural place to inspect assignment and experiment-specific results, while Analytics can provide broader behavioral context. It does not make either system automatically authoritative for every metric. Google’s explanation is in its GA4 A/B test guidance.
Before operationalizing a result, compare tools on the measurement details that affect your decision:
- How assignment and exposure are defined.
- Which identity unit is counted and how duplicates are handled.
- How events, conversions, and revenue metrics are calculated.
- How attribution windows and report dates work.
- Which statistical method is used and what its intervals mean.
- Whether sampling, modeling, or processing latency applies.
- Whether assignment and event-level data can be exported for an independent check.
Google’s documentation establishes that these differences exist; it does not rank vendors or establish that one product’s results are generally more accurate. There is also no general prevalence figure in the cited official sources for how often experimentation platforms and analytics disagree.
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