HN Books @HNBooksMonth

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

Depending on the context, I'm a fan of the work of Douglas Hubbard, in his book How to Measure Anything[1]. His approach involves working out answers to things that might sometimes be done as a "back of the napkin" kind of thing, but in a slightly more rigorous way. Note that there are criticisms of his approach, and I'll freely admit that it doesn't guarantee arriving at an optimal answer. But arguably the criticisms of his approach ("what if you leave out a variable in your model?", etc.) apply to many (most?) other modeling approaches.

On a related note, one of the last times I mentioned Hubbard here, another book came up in the surrounding discussion, which looks really good as well. Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin[2] - I bought a copy but haven't had time to read it yet. Maybe somebody who is familiar will chime in with their thoughts?

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

[2]: https://www.amazon.com/gp/product/0691129495/ref=ppx_yo_dt_b...

⬐ dr_dshiv

Let me second "How to measure anything." I think it should be required reading for human beings.

⬐ alex_anglin

I would add that "How the Measure Anything in Cybersecurity Risk" should be a core part of Infosec literature.

⬐ ingqondo

How to Measure Anything is a fantastic book. Here are the most significant insights you learn in the book

- how to measure anything; Hubbard actually comes through on the promise of the title - after finishing the book you will truly feel that the scope of what you can measure is massive. He does this by a change in the definition of what it means to measure something, but you realize his definition is more correct than the everyday intuitive one.

- value of information; Hubbard gives a good introduction to the VOI concept in economics, which basically lets you put a price on any measurement or information and prioritize what to measure

- motivation for 'back of the napkin' calcs; through his broad experience he has seen how a lot of the most important things that affect a business go unmeasured, and how his approach to 'measuring anything' can empower people to really measure what matters.

Reading this book provided one half of what I have been searching for for a long time - a framework for thinking about data science activities which is not based on hype, fundamentally correct and still intuitive and practical.

I don't think that we're on the same wavelength and no, we are not at all agreed on what a metric is, but here's a link to a book that might be interesting:

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

⬐ inimino

I don't know your working definition of a metric, but if you think it is coextensive with "determining the consequences of your actions" then I'm sure it's not the right definition in this context. If you think it's necessary to measure before choosing a right course of action in every circumstance, I think you'll find that belief dissolves under a little scrutiny.

From the reviews, the book seems to have some light statistics and introduction to Monte Carlo methods. I don't think it would have much to teach me about statistics or philosophy. It might be useful for someone without a background in statistics who is forced to navigate a business environment where people value metrics above all else, but I'm arguing against such environments.

⬐ perl4ever

"I don't know your working definition of a metric, but if you think it is coextensive with "determining the consequences of your actions" then I'm sure it's not the right definition in this context"

You wrote "In most situations in life, you must make a decision without any chance to know the results of your actions". So it seems to me you were clearly expressing an idea of what a metric is, in rejecting the need for metrics, that you now attribute to me and say it's the wrong definition.

Unless I'm missing a subtlety like perhaps consequences are not results?

I agree that you probably wouldn't learn anything from that book.

⬐ inimino

> So it seems to me you were clearly expressing an idea of what a metric is, in rejecting the need for metrics, that you now attribute to me and say it's the wrong definition.

No. You said a bunch of things, that to me are almost entirely unrelated, and I responded to several of them.

One of them was about determining the consequences of actions and I responded to that because it was part of your argument about having personal values.

Before any of that, you wrote: "How can one "fill the void" without measuring anything? Personal values may vary, but I can't see the concept of determining the consequences of your actions as being an optional one of them."

The implication is that if you reject metrics you still have to measure and determine the consequences of your actions, so metrics are unavoidable. If metrics are not the same thing as "determining the consequences of your actions" to you, then you answered your own question of how to "fill the void": you determine the consequences of your actions without metrics.

The question of whether metrics are a good way to determine the consequences of actions is separate from the question of consequentialism, or whether actions should be chosen by looking at consequences alone. You seem to be assuming both and I'm challenging both.

⬐ perl4ever

"No."

You weren't clearly expressing an idea of what a metric is, gotcha. Very helpful.

IIRC the book "How to measure anything" contains some advice like this: https://www.amazon.com/dp/1118539273/

The author also offers webinars, so maybe it was from him: https://www.howtomeasureanything.com

⬐ mettamage

I think it was from a pretty prominent researcher that had a .edu site, but I couldn't find it and I really tried.

I also like this guide: https://www.av8n.com/physics/thinking.htm "Learning, Remembering, and Thinking". I recommend checking out his other work for a model of working through problems coming from physicists.

One more thing. Oftentimes the key step to thinking is figuring out what you're questions are, and questions are always determined by what uncertainties you have in a domain, as specifically relevant as you can make them.

I'm gonna quote Venkat Rao (of Breaking Smart and Ribbonfarm fame) from an article he deleted years ago:

> Real questions, useful questions, questions with promising attacks, are always motivated by the specific situation at hand. They are often about situational anomalies and unusual patterns in data that you cannot explain based on your current mental model of the situation… Real questions frame things in a way that creates a restless tension, by highlighting the potentially important stuff that you don’t know. You cannot frame a painting without knowing its dimensions. You cannot frame a problem without knowing something about it. Frames must contain situational information. There are two types of questions. Formulaic questions and insight questions. …. Formulaic questions can be asked without knowing much. If they can be answered at all, they can be answered via a formulaic process. …. Insight questions can only be asked after you develop situation awareness. They are necessarily local and unique to the situation.

The world is /extremely/ information rich to the point of absurdity, and what fails is not the richness of our input data but rather our awareness of how we ought to use it. George Polya tried to teach his students how to problem solve in mathematics by means of getting people to ask questions. By verbalizing his thought process he hoped to convey these principles, as well as giving them a standard template to prompt their cycle of questions. But to adhere to a strict plan like that is to defeat the point. The real point is to maintain a conversation with yourself, giving yourself and refining your own questions until insight develops, and keeping yourself talking.

Ultimately I like to take an information-theoretic approach as the basis of my philosophy here. /Some/ information is /always/ going to be contained in /any/ comparison that I can make between two phenomena in the world. Most of this "information" would be considered noise relative to most reference frames. But it is always possible to extract /something/ from a situation by creating these tensions between yourself and your uncertainties in the world.

You can muddle around questioning things for awhile, but gradually things come up. The key is to let your uncertainty start off however it is and keep pruning away at it until your solution is sculpted from the clay. It can and will happen.

If you've ever tried doing Fermi Estimates (like those prescribed in https://www.amazon.com/Street-Fighting-Mathematics-Educated-... , https://www.amazon.com/Art-Insight-Science-Engineering-Compl... , https://web.archive.org/web/20160309161649/http://www.its.ca... , https://www.amazon.com/How-Measure-Anything-Intangibles-Busi...), then you'll be able to perceive the mindset that has significant transfer to many problems that have even just approximate answers.

⬐ mezod

thank you

If they could be easily derived, we'd all be doing it all the time. Spend some time doing it before you apply for your next job (or salary review) and you might be pleasantly surprised at how well the conversation goes. I linked to "How to Measure Anything" in another comment, and that's a good read - https://www.amazon.co.uk/How-Measure-Anything-Intangibles-Bu....

If you really can't find a way for your current job, then say how many downloads your open source project has got. Or how many comments or page views your blog gets. For some reason, employers get excited when I tell them "I'm in the top 3% on StackOverflow". (Yes, I know how ridiculous that sounds.)

But that guy who earns twice as much you and does half the work? This is what he does. He talks in the language of the people who decide his salary, and that language involves specific numbers that matter to the business.

You're absolutely right! That's another thing - always get someone to proofread for you.

> bullshitting your way through the CV-based selection ... I don't think it's what you had in mind.

That's _exactly_ what I had in mind, though I might phrase it as "honing your sales pitch". On something like a CV, you should always be happy to lose a little accuracy and completeness to gain some clarity. That doesn't mean you can make stuff up, but you do need to phrase yourself carefully to highlight your strong points.

You'd be surprised at what you can quantify. See "How to Measure Anything (https://www.amazon.co.uk/How-Measure-Anything-Intangibles-Bu... for some ideas. Measurables are the language of those who hire you (and decide your salary) so learning how to show this kind of stuff is essential.

* What was the conversion rate on that page you made before and after?

* How long did it take staff in your company to complete that process before and after you rebuilt it? How many hours have you saved the company?

* How many bugs were reported per day for that module before you fixed it, and how much did it cost the business to process them?

I'm a huge believer in going back to primary texts, and understanding where ideas came from. If you've liked a book, read the books it references (repeat). I also feel like book recommendations often oversample recent writings, which are probably great, but it's easy to forget about the generations of books that have come before that may be just as relevant today (The Mythical Man Month is a ready example). I approach the reading I do for fun the same way, Google a list of "classics" and check for things I haven't read.

My go to recommendations:

http://www.amazon.com/Structure-Scientific-Revolutions-50th-... - The Structure of Scientific Revolutions, Thomas Kuhn, (1996)

http://www.amazon.com/Pragmatic-Programmer-Journeyman-Master... - The Pragmatic Programmer, Andrew Hunt and David Thomas (1999)

Things I've liked in the last 6 months:

http://www.amazon.com/How-Measure-Anything-Intangibles-Busin... - How to Measure Anything, Douglas Hubbard (2007)

http://www.amazon.com/Mythical-Man-Month-Software-Engineerin... - Mythical Man Month: Essays in Software Engineering, Frederick Brooks Jr. (1975, but get the 1995 version)

http://www.amazon.com/Good-Great-Some-Companies-Others/dp/00... - Good To Great, Jim Collins (2001)

Next on my reading list (and I'm really excited about it):

http://www.amazon.com/Best-Interface-No-brilliant-technology... - The Best Interface is No Interface, Golden Krishna (2015)

⬐ walterbell

Your list of primary sources ends at 2001 :)

No classics beyond that date?

⬐ lostphilosopher

The top was, "choose your own adventure" advice, the bottom was, "here's some clickable immediate gratification" advice. But point well taken, haha.

⬐ jon-wood

That reminds me, I'm due a re-read of The Pragmatic Programmer. I re-read it about once a year, and every time seem to get something new from it.