Name: How to Measure Anything
ISBN: 9781118539279

How to Measure Anything

Now updated with new measurement methods and new examples, How to Measure Anything shows managers how to inform themselves in order to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction Simplifies overall content while still making the more technical applications available to those readers who want to dig deeper Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas Offers practical methods for measuring a variety of "intangibles" Provides an online database (www.howtomeasureanything.com) of downloadable, practical examples worked out in detailed spreadsheets Written by recognized expert Douglas Hubbard—creator of Applied Information Economics—How to Measure Anything, Third Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using…

What Hacker News says

rramadassSep 2026

Yes, there is always a need for another "intuitive statistics" book.

However the link you have provided is not the way; it is low on content and high on pretty distractions. Use all sorts of diagrams and graphs primarily, with animations only where required. The key is to always relate to something in the real world so one can see its actual relevance. Also tie it back to other fields of mathematics so one can see how they all come together.

A good example to study is How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard. Detailed review at - https://www.lesswrong.com/posts/ybYBCK9D7MZCcdArB/how-to-mea...

And of course Nassim Taleb's works are a good source of inspiration. Here is a great video summarizing Taleb's ideas nicely Pareto, Power Laws, and Fat Tails - https://www.youtube.com/watch?v=Wcqt49dXtm8

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bobbiechenAug 2025

_How to Measure Anything_ by Douglas Hubbard includes a chapter on Monte Carlo simulations and comes with downloadable Excel examples: https://www.howtomeasureanything.com/3rd-edition/ (scroll down to Ch. 6)

The main example is, you're considering leasing new equipment that might save you money. What's the risk that it will actually cost more, considering various ranges of potential numbers (and distributions)?

I think it's harder to apply to software since there are more unknowns (or the unknowns are fatter-tailed) but I still liked the book just for the philosophical framing at the beginning: you want to the measure things because they help you make decisions; you don't need perfect measurements since reducing the range of uncertainty is often enough to make the decision.

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mindcrimeMar 2025

There's no "model" there, just a methodology. Hubbard does consulting and sells books, that's how he monetizes this as far as I know. His book How to Measure Anything[1] lays out the core ideas in sufficient detail for somebody to implement the basic idea. A used copy could be acquired for < $10 if one were interested in learning more.

[1]: https://www.amazon.com/How-Measure-Anything-Intangibles-Busi...

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esafakSep 2024

You can estimate: https://www.wiley.com/en-us/How+to+Measure+Anything+in+Cyber...

It's just that it is not the norm, so expectations are low. Some companies consistently do better than others, so clearly it's not all down to luck.

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chuanklAug 2024

I am not the person you asked, but I have been on a similar path to improve my statistical literacy. For context, I am fairly good at math generally (used to be a math major in college decades ago; didn't do particularly well though I did graduate) but always managed to get myself extremely confused when it comes to statistics.

In terms of books: there are a few good ones aimed for the general public, such as The Signal and The Noise. How to Measure Anything: Finding the Value of Intangibles in Business is a good book of applying statistical thinking in a practical setting, though it wouldn't help you wrap your brain around things like the Monty Hall problem.

The one book that really made things click for me was this:

Probability Theory: The Logic of Science by E. T. Jaynes

This book is a bit more math-heavy, but I think anyone with a working background in a science or engineering field (including software engineering) should be able to get the important fundamental idea out of the book.

You don't need to completely comprehend all the details in math (I surely didn't); it is enough to have a high-level understanding of how the formulas are structured at the high level. But you do need enough math (for example, an intuitive understanding of logarithm) for the book to be useful.

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kqrAug 2024

I second both The Signal and the Noise as well as How to Measure Anything. I also mentioned upthread Willful Ignorance.

I think perhaps the best bang for your buck could be Wheeler's Understanding Variation -- but that is based mainly on vague memory and skimming the table of contents. I plan on writing a proper review of that book in the coming year to make certain it is what I remember it to be.

I think the earlier works by Taleb also touch on this (Fooled by Randomness seems to have it in the title).

But then I strongly recommend branching out to places where these fundamentals are used, to cement them:

- Anything popular by Deming (e.g. The New Economics)

- Anything less popular by Deming (e.g. Some Theory of Sampling)

- Moneyball

- Theory of Probability (de Finetti)

- Causality (Pearl)

- Applied Survival Analysis

- Analysis of Extremal Events

- Regression Modeling with Actuarial and Financial Applications

The more theoretical and applied books are less casual reads, obviously. They also happen to be the directions in which I have gone -- you may have more luck picking your own direction for where to apply and practice your intuition.

Edit: Oh and while I don't have a specific book recommendation because none of the ones I read I have good opinions on, something on combinatorics helps with getting a grasp on the general smell of entropy and simpler problems like Monty Hall.

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