Reading in Progress
These are active notes while I make my way through the book. Expect updates, revisions, and maybe contradictions until I finish.
Summary in Four Sentences (or Less)
TBD
Content coming soon, after I finish reading.
Chapter Map
Who Should Read It
TBD
Content coming soon, after I finish reading.
Book Discovery
I discovered How to Measure Anything while searching for better ways to articulate and justify the value I bring as a Technical Product Owner. Hubbard’s core argument, that anything that matters can in fact be measured, offered a framework for translating my intangible contributions into evidence-based results.
Impact
TBD
Content coming soon, after I finish reading.
Favorite Quotes
Atomic Notes
WIP
Some puzzle pieces haven’t been snapped in yet. As I process these notes into atomic form, links will start appearing to connect the bigger picture.
Chapter 1: The Challenge of Intangibles
- Intangibles Are Not Truly Immeasurable
- Two Uses of “Intangible”
- Consequences of Believing in Immeasurability
- Soft Benefits Can Be Quantified
- Measurement Guides Decision-Making
- Three Principles of Measurement
- Measurement as an Optimization Problem
- Connection Between Measurement and Decision Analysis
- Simplicity Over Complexity
- Universal Application of Measurement Steps
- Challenging Perceived Immeasurables
- Economic Value of Measurement
- Structured Process for Measurement
- Tools and Support for Application
Chapter 2: An Intuitive Measure Habit
- Intuitive Measurement Mindset
- Eratosthenes’ Simple Geometry
- Lesson From Eratosthenes
- Fermi’s Quick Estimates
- Uncertainty Exposure Via Decomposition
- Chicago Piano Tuners Example
- Market Sizing by Fermi
- Emily’s Controlled Test
- Low-Cost, High-Clarity Experiments
- Testing Intangibles
- Measurement vs. Immeasurability
- Uncertainty Reduction Framing
- Executive Comprehension Myth
- Practical Next Steps
- Three Exemplars, Three Lessons
Chapter 3: The Illusion of Intangibles
- Everything Is Measurable
- Measurement Reduces Uncertainty
- More Than Quantities
- Information Has Value
- Clarify What You Mean
- Decision First, Then Measure
- Three Objections Sorted
- Value of Information Focus
- Small Samples, Big Learning
- Methods for the “Immeasurable”
- Measure, Then Refine
- Four Useful Assumptions
- Outcomes Over Incentives
- Stats Misconceptions Debunked
- Ethics and Tradeoffs
- Universal Five-Step Flow
- Proxies That Matter
- Sampling Hidden Data
- Thought Experiments Help
- Measurement, Not Perfection
Chapter 4: Clarifying the Measurement Problem
- Decision-Driven Measurement
- Define the Measurable Thing
- Link Measure to Decision Impact
- Assess Current Uncertainty
- Value of Information Focus
- Clarifying Changes What to Measure
- Zero-Value Reporting Example
- Uncertainty vs. Risk Defined
- From Inputs to Outcomes in Security
- VA Portfolio Clarification
- Fermi Questions for Costing
- Calibration for Probabilities
- The AIE Starter Questions
- Language Enables Measurement
Chapter 5: Calibrated Estimates
- Confidence Intervals Defined
- Calibration Explained
- Overconfidence Is Common
- Equivalent Bet Test
- Two Extremes of Confidence
- Calibration Quiz Mechanics
- Expected vs. Actual Scores
- Absurdity Test Method
- Avoiding Anchors
- Pros and Cons Check
- Training That Works
- Transfer to Real Decisions
- Evidence From Industry Test
- Who Improves, Who Doesn’t
- Conceptual Obstacles
- Subjectivist Interpretation
- Practical Payoff
- Value of Knowing Uncertainty
Chapter 6: Quantifying Risk through Modeling
- Uncertainty vs. Risk
- Qualitative Labels Mislead
- Monte Carlo, Not Point Estimates
- Spreadsheet-Friendly Ranges
- Breakeven as Risk Definition
- Avoid Extreme Cases
- Summing Normal Ranges
- Modeling Discrete Shocks
- Placebo of Soft Scoring
- Risk Preferences Matter
- The Risk Paradox
- Evidence It Works
- Probability Management
- Calibrate Estimators
- Point Estimates Hide Risk
Chapter 7: Quantifying the Value of Information
- Why Information Has Value
- Risk as Chance × Cost
- Value Equals Risk Reduction
- Ceiling on Measurement Spend
- Binary Decision Example
- Ranges, Not Just Yes/No
- Relative Threshold (RT)
- Quick EVPI Approximation
- Imperfect Information Counts
- Diminishing Returns
- Biggest Jump to “No Loss”
- Small Data, Big Uncertainty
- Measure What Matters
- IT Case in Point
- Incentive Value of Metrics
- Market Value of Data
- Iterative by Default
- Cost in Context
- Epiphany Equation
- First Things First
Chapter 8: The Transition: From What to Measure to How to Measure
- From Decision to Measurement
- Empirical Methods, Broadly
- Instruments’ Six Advantages
- Decomposition Narrows Ranges
- Secondary Research First
- Observation Method Cascade
- Trails and Tracers
- Measure Just Enough
- Two Error Types
- Accuracy vs. Precision
- Control Key Biases
- Random Beats Biased
- Instrument Design Steps
- Iterate And Combine
- Reason From Consequences
- Decomposition Suffices Sometimes
Chapter 9: Sampling Reality
Chapter 10: Bayes: Adding to What You Know Now
Chapter 11: Preference and Attitudes: The Softer Side of Measurement
Chapter 12: The Ultimate Measurement Instrument: Human Judges
Chapter 13: New Measurement Instruments for Management
Chapter 14: A Universal Measurement Method: Applied Information Economics
Lingering Questions
Works Consulted
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