The four questions Early Warning asks before any A/B test

Priya Singhee, VP of Enterprise Analytics & Data Science at Early Warning and former Wayfair analytics leader, argues that effective experimentation depends less on launching A/B tests than on determining whether they can be conducted validly. Her framework emphasizes clean randomization, alignment between randomization and measurement units, sufficient traffic and plausible effect sizes, reversible changes, and a specific falsifiable hypothesis. Because an estimated 85–90% of tests fail, she advises organizations to treat experimentation as a learning agenda rather than rewarding only positive results, since unusually high win rates may indicate flawed measurement or false positives. Singhee recommends pre-registering hypotheses, metrics, statistical methods, planned subgroup analyses, decision rules, and kill criteria to limit p-hacking and post-hoc interpretation, while documenting all experiments and lessons companywide. She maintains that testing prevents costly feature launches by identifying losing ideas before they harm revenue, making loss avoidance and organizational learning more valuable than maximizing apparent test wins.

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