[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_29":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5,"_12":-5},"book",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24},"currentUser","formToken","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQ1NTA","title","DATA-INFORMED, NOT DATA BLIND","state","published","contactAuthorMethod","email","aboutText","

Most product teams don't lack data. They lack a disciplined relationship with it — and that gap quietly wastes more roadmap time than any single bad feature ever will.

Data-Informed, Not Data-Blind is a practitioner's guide to analytics and experimentation for product managers who want to move past dashboard theater and into genuinely rigorous decision-making. It starts by dismantling the vanity metrics and single \"North Star\" numbers that hide more than they reveal, then walks through what it actually takes to run trustworthy experiments: statistical significance without the misuse, power and sample size before you launch, novelty effects that make week-one results lie, and the situations — marketplaces, enterprise sales cycles, network effects — where A/B testing breaks down entirely and something else is needed instead.

The second half of the book moves from technique to practice: auditing instrumentation before it silently corrupts every metric downstream, briefing data scientists so they don't hand you the wrong analysis, navigating the politics of telling leadership their bet was wrong, and building team rituals — pre-mortems, kill criteria, results reviews — that make rigor the default instead of something one person has to enforce every time.

Each chapter follows the same practitioner pattern: core concepts, frameworks and diagrams you can actually use, worked numerical examples, honest trade-offs, common failure patterns, and a checklist to take back to your team. The book closes with four detailed case studies — three bets that looked right and weren't, and one that looked wrong and was right — that tie every earlier concept together.

Written for mid-to-senior PMs, team leads, and anyone who's tired of \"the data says\" being the end of a conversation instead of the start of a better one.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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