Designing Useful Measurement Systems
Design useful measurement systems by measuring outcomes not activities, using leading and lagging indicators together, and building in resistance to gaming.
Welcome to the complete index of every article in our Metrics Measurement Evaluation collection on When Notes Fly. This page lists all 11 articles in the section, organized alphabetically for easy reference. Each piece is researched, written by hand, and grounded in academic sources, professional practice, or empirical data. Whether you are diving into Metrics Measurement Evaluation for the first time or returning to find a specific article, the index below gives you direct access to the full collection within Concepts.
If you are new to Metrics Measurement Evaluation, we recommend starting with the foundational explainers and definitions before moving on to specific case studies, applied frameworks, and deeper analytical pieces. Articles are written for thoughtful readers who want substance over summary, with clear explanations of how ideas connect, where they come from, and why they matter. Use this index as a navigational map: skim the titles, read the short summaries, and click through to the pieces that draw your interest. Each article also links to related material so you can follow a thread of ideas across our entire Concepts library.
Design useful measurement systems by measuring outcomes not activities, using leading and lagging indicators together, and building in resistance to gaming.
When a measure becomes a target, it ceases to be a good measure. People optimize for metrics, not goals, creating distortion and gaming.
Interpret data correctly by avoiding confirmation bias, p-hacking, confusing correlation with causation, and survivorship bias in your analysis.
KPIs (Key Performance Indicators) are the few metrics that actually matter for your goals. Not all metrics are KPIs—only those that drive real decisions.
Measurement bias: Systematic error in data collection distorting results consistently (not random noise—predictable direction). Types: 1) Selection bias (w.
Measurement bias: Systematic error in data collection distorting results consistently (not random noise—predictable direction). Types: 1) Selection bias (w.
Quantitative metrics measure numbers like revenue and time. Qualitative metrics assess quality like feedback and satisfaction. Both are needed together.
Vanity metrics look impressive but don't drive decisions: total users, page views. Meaningful metrics change behavior: active users, retention, revenue.
Measure what drives outcomes, not what's easy to measure. Focus on outcomes over activities, and use leading indicators to predict future results.
What gets measured gets optimized. Measurement creates visibility, accountability, and focuschanging behavior whether intended or not.
Metrics mislead through gaming the numbers, proxy failure not representing what matters, context loss, and aggregation hiding important details.
« Back to Metrics Measurement Evaluation · All Concepts Articles · Home