💡TL;DR: Don’t blindly wait for 95% stat sig. Use statistical confidence as needed—tailored to context, impact, and product risk. Prioritize action, not perfection. Understanding the Statistical Models Behind A/B Testing While PMs don’t need to be statisticians, knowing the types of statistical models used in

TL;DR: Metrics guide your team’s decisions, but the wrong ones can mislead. This post breaks down how to define the right KPIs and structure them effectively for live-service games. Series: Data as a North Star – Part 2 of 5 In live-service games, strong metrics help teams prioritize, evaluate

TL;DR: Every great experiment starts with a great question. A clear hypothesis keeps your team focused on learning — not just launching. This post breaks down how to write sharper, testable hypotheses in the messy world of live games. Series: Data as a North Star – Part 1 of 5 Most

Most teams ship features without knowing what success looks like. Then they scramble to interpret results after launch. But what if you could define your success criteria before you even start building? Expected Outcomes aren’t just a reporting tool — they’re a leadership mindset. They create clarity, drive alignment,