Shopify A/B Testing: Priorities, Sample Size, and Stop Rules
Run Shopify CRO through experiment hard rules before A/B testing: low sample is not a formal conclusion, no-peeking means no early stop, MDE / power / baseline CVR must be explainable, and traffic, discount, inventory, payment, speed, and tracking counter-evidence must be cleared before choosing formal A/B, directional validation, qualitative validation, or direct repair.
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Ranfeng WeiPublished
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Review scope This lesson maintains 7 linked references; recheck current platform, account, and market details before acting.
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Run Shopify CRO through experiment hard rules before A/B testing: low sample is not a formal conclusion, no-peeking means no early stop, MDE / power / baseline CVR must be explainable, and traffic, discount, inventory, payment, speed, and tracking counter-evidence must be cleared before choosing formal A/B, directional validation, qualitative validation, or…
A Shopify CRO series for teams with traffic but unstable conversion. Learn funnel roles, PDP trust, collection navigation, cart and checkout friction, landing-page message match, behavior evidence, A/B testing discipline, and monthly CRO roadmaps.
Lesson outline
- 1Write one falsifiable hypothesis
- 2Decide whether this is an experiment issue
- 3Lock one main variable and primary metric
- 4Use the Sample Reality Calculator to unpack sample reality
- 5Write freeze rules
- 6Pre-write readout and stopping discipline
- 7Classify readout with the Decision Ledger
- 8Write back to notes and monthly roadmap
Public core framework
- Write one falsifiable hypothesis from behavior evidence or GA4/Shopify breaks.
- Decide whether this is an experiment issue or a bug, payment, discount, privacy, tracking, or stock issue.
- Lock one main variable, one primary metric, and no more than two guardrails.
Sources and review
Last checked: 2026-10-03- arxiv.orghttps://arxiv.org/abs/2312.10814
- support.optimizely.comhttps://support.optimizely.com/hc/en-us/articles/4410284003341-Statistical-significance
- www.optimizely.comhttps://www.optimizely.com/optimization-glossary/ab-testing/
- certification.optimizely.comhttps://certification.optimizely.com/docs/concepts/experimentation/stats-engine-deep-dive/
- support.optimizely.comhttps://support.optimizely.com/hc/en-us/articles/4410288998797-Experiment-types-in-Web-Experimentation
- doi.orghttps://doi.org/10.1080/00031305.2023.2257237
- arxiv.orghttps://arxiv.org/abs/2602.05997
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Reviewed by: Ranfeng Wei · Next review: 2027-04-01
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
- Public facts and operating boundaries in this preview→source
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