Meta-Analysis

Meta-analysis is the practice of combining results from multiple independent studies to find stronger, more reliable patterns than any single study can provide. Individual studies are noisy — small samples, local conditions, methodological quirks. By pooling them systematically, meta-analysis boosts signal and dampens noise. The result is a higher-confidence answer to the question "does this actually work?" For founders, the mental model is: don't trust a single data point, experiment, or case study. Aggregate across multiple sources before drawing conclusions. One positive result is an anecdote; a pattern across many is evidence.

When making a decision, aggregate evidence from multiple sources rather than relying on the most recent or most vivid data point. For product decisions: combine multiple A/B tests, cohort analyses, and qualitative inputs before concluding. For strategy: look at patterns across multiple markets, competitors, and time periods. Weight evidence by quality and relevance. The point isn't consensus — it's synthesis.

Meta-analysis: combine results from multiple studies or experiments to find stronger patterns than any single source can provide. Don't trust one data point — aggregate across many. Signal survives aggregation; noise doesn't.