Why Do Experiment Metrics Differ from Funnel Metric Results?
why experiments metrics result differ from the same metric funnel results?
why experiments metrics result differ from the same metric funnel results?
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Experiment metrics and Funnel metrics can differ because they’re calculated in different ways and often over different populations.Key reasons from the docs: 1. Population / attribution differences
- Experiments only consider users after they fire the exposure event ($experiment_started) and attribute all later events to the variant they saw. [Analysis engine]
- Funnels simply look at whoever did the funnel steps in the date range, regardless of experiment exposure.2. Metric definition & normalization
- In Experiments, “group rate” is always normalized by exposed users, not by converters or raw event counts. [Read lift]
- Funnels can be configured as uniques, total conversions, sessions, etc., and “totals” in Funnels are total conversions, not total events. [Funnels FAQ]3. Statistical model & variance
- Experiments use specific statistical models (Poisson/Bernoulli/normal) and compute p‑values, confidence intervals, and lift based on those assumptions. [Interpret results; Read lift]
- Funnels don’t apply this experiment-specific normalization and variance modeling; they just compute the funnel metric directly.4. Counting logic & re-entry
- Funnels have their own rules for re-entry, conversion windows, optimized re-entry, and how multiple attempts are counted. [Optimized re-entry; Funnels FAQ]
- Experiments look at all behavior after exposure (up to 90 days) and attribute it to the variant, independent of funnel re-entry rules. [Analysis engine]5. Query / filter differences
- As with other report discrepancies, even small differences in filters, properties (event vs user), time ranges, or aggregations (uniques vs totals) will cause mismatches. [Data discrepancies]If you want them closer, align: - Same user set (e.g., filter Funnels to users who fired $experiment_started for that experiment/variant).
- Same metric definition (conversion vs total events, uniques vs totals).
- Same time range and filters.
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