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• How to Measure Anything
• Contents
• Preface to the Third Edition
• About the Companion Website
• Acknowledgments
• About the Author
• Part I The Measurement Solution Exists
• Chapter 1 The Challenge of Intangibles
• The Alleged Intangibles
• Yes, I Mean Anything
• The Proposal: It’s about Decisions
• A “Power Tools” Approach to Measurement
• A Guide to the Rest of the Book
• Chapter 2 An Intuitive Measurement Habit: Eratosthenes, Enrico, and Emily
• How an Ancient Greek Measured the Size of Earth
• Estimating: Be Like Fermi
• Experiments: Not Just for AduLts
• Notes on What to Learn from Eratosthenes, Enrico, and Emily
• Notes
• Chapter 3 The Illusion of Intangibles: Why Immeasurables Aren’t
• The Concept of Measurement
• A Definition of Measurement: An “Information Theory” Version
• A Variety of Measurement Scales
• Bayesian Measurement: A Pragmatic Concept for Decisions
• The Object of Measurement
• The Methods of Measurement
• The Power of Small Samples: The Rule of Five
• Even Smaller Samples: The Urn of Mystery
• Our Small-Sample Intuition versus Math
• Economic Objections to Measurement
• The Broader Objection to the Usefulness of “Statistics”
• Ethical Objections to Measurement
• Reversing Old Assumptions
• It’s Been Measured Before
• You Have Far More Data than You Think
• You Need Far Less Data than You Think
• Useful, New Observations Are More Accessible than You Think
• Notes
• Part II Before You Measure
• Chapter 4 Clarifying the Measurement Problem
• Toward a Universal Approach to Measurement
• The Unexpected Challenge of Defining a Decision
• Decision-Oriented Measurements: For Scientists, Too
• How to Get to a Real Decision
• Requirements for a Decision
• Potential Forms of a Decision
• If You Understand it, You Can Model it
• Getting the Language Right: What “Uncertainty” and “Risk” Really Mean
• An Example of a Clarified Decision
• Notes
• Chapter 5 Calibrated Estimates: How Much Do You Know Now?
• Calibration Exercise
• Calibration Trick: Bet Money (or Even Just Pretend To)
• Further Improvements on Calibration
• Conceptual Obstacles to Calibration
• The Effects of Calibration Training
• Notes
• Chapter 6 Quantifying Risk through Modeling
• How Not to Quantify Risk
• Real Risk Analysis: The Monte Carlo
• An Example of the Monte Carlo Method and Risk
• Tools and Other Resources for Monte Carlo Simulations
• The Risk Paradox and the Need for Better Risk Analysis
• Notes
• Chapter 7 Quantifying the Value of Information
• The Chance of Being Wrong and the Cost of Being Wrong: Expected Opportunity Loss
• The Value of Information for Ranges
• Beyond yes/no: Decisions on a Continuum
• The Imperfect World: The Value of Partial Uncertainty Reduction
• Perishable Information Values
• Information Values for Multiple Variables
• The Epiphany Equation: How the Value of Information Changes Everything
• Summarizing Uncertainty, RisK, and Information Value: The pre-measurements
• Notes
• Part III Measurement Methods
• Chapter 8 The Transition: From What to Measure to How to Measure
• Tools of Observation: Introduction to the Instrument of Measurement
• Decomposition
• Secondary Research: Assuming You Weren’t the First to Measure It
• The Basic Methods of Observation: If One Doesn’t Work, Try the Next
• Measure Just Enough
• Consider the Error
• Choose and Design the Instrument
• Note
• Chapter 9 Sampling Reality: How Observing Some Things Tells Us about All Things
• Building an Intuition for Random Sampling: The Jelly Bean Example
• A Little About Little Samples: A Beer Brewer’s Approach
• Are Small Samples Really “Statistically Significant”?
• When Outliers Matter Most
• The Easiest Sample Statistic Ever
• A Biased Sample of Sampling Methods
• Population Proportion Sampling
• Spot Sampling
• Serial Sampling
• Measure to the Threshold
• . . . And a Lot More
• Experiment
• An Example Experiment
• Now, More about the Meaning of Significance
• The Significance of Emily Rosa’s Experiment: A Counterfactual Outcome
• Seeing Relationships in the Data: An Introduction to Regression modeling
• A Regression Example: TV Ratings
• Parting Thoughts About Regression
• Notes
• Chapter 10 Bayes: Adding to What You Know Now
• The Basics and Bayes
• Example: Applying Bayes to Market Tests of New Products
• One More Time: A Bayesian Look at Emily’s Experiment
• Demystifying the Urn of Mystery
• Using Your Natural Bayesian Instinct
• Instinctive Bayesian Approach
• Heterogeneous Benchmarking: A “Brand Damage” Application
• Bayesian Inversion for Ranges: An Overview
• Example: Percentage of Customers Kept After a Change
• Bayes for Estimates of Means
• The Lessons of Bayes
• Myth 1: Absence of Evidence
• Myth 2: Correlation Is Not Evidence of Causation
• Myth 3: Ambiguous Results Tell Us Nothing
• Myth 4: “This Alone Tells Me Nothing”
• Notes
• Part IV Beyond the Basics
• Chapter 11 Preference and Attitudes: The Softer Side of Measurement
• Observing Opinions, Values, and the Pursuit of Happiness
• A Willingness to Pay: Measuring Value via Trade-Offs
• Putting It All on the Line: Quantifying Risk Tolerance
• Quantifying Subjective Trade-Offs: Dealing with Multiple Conflicting Preferences
• Keeping the Big Picture in Mind: Profit Maximization Versus Purely Subjective Trade-Offs
• Notes
• Chapter 12 The Ultimate Measurement Instrument: Human Judges
• Homo Absurdus: The Weird Reasons behind Our Decisions
• Getting Organized: A Performance Evaluation Example
• Surprisingly Simple Linear Models
• How to Standardize Any Evaluation: Rasch Models
• Removing Human inconsistency: the Lens Model
• Panacea or Placebo?: Questionable Methods of Measurement
• Comparing the Methods
• Example: A Scientist Measures the Performance of a Decision Model
• Notes
• Chapter 13 New Measurement Instruments for Management
• The Twenty-First-Century Tracker: Keeping Tabs with Technology
• Measuring the World: The Internet as an Instrument
• Prediction Markets: A Dynamic Aggregation of Opinions
• Notes
• Chapter 14 A Universal Measurement Method: Applied Information Economics
• Bringing the Pieces Together
• Case: The Value of the System That Monitors Your Drinking Water
• Phase 0
• Phase 1
• Phase 2
• Phase 3
• Epilogue
• Case: Forecasting Fuel for the Marine Corps
• Phase 0
• Phase 1
• Phase 2
• Phase 3
• Epilogue
• Case: Measuring the Value of Acord Standards
• Phase 0
• Phase 1
• Phase 2
• Phase 3
• Epilogue
• Ideas for Getting Started: A Few Final Examples
• Quality
• Value of a Process, Department, or Function
• Innovation
• Information Availability
• Flexibility
• Flexibility with Options Theory
• Summarizing the Philosophy
• Notes
• Appendix: Calibration Tests (and Their Answers)
• Index