Quotes from Douglas W. Hubbard

If a measurement matters at all, it is because it must have some conceivable effect on decisions and behaviour. If we can't identify a decision that could be affected by a proposed measurement and how it could change those decisions, then the measurement simply has no value

As far as the propositions of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality. —Albert Einstein (1879–1955)

If you know almost nothing, almost anything will tell you something.

Essentially, all models are wrong, but some are useful.

It is better to be approximately right than to be precisely wrong. —Warren Buffett

Although this may seem a paradox, all exact science is based on the idea of approximation. If a man tells you he knows a thing exactly, then you can be safe in inferring that you are speaking to an inexact man. —Bertrand Russell

When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science. —Lord Kelvin (1824–1907),

Perhaps the biggest misconception some managers may run into is the belief that correlation proves causation. The fact that one variable is correlated to another does not necessarily mean that one variable causes the other. If church donations and liquor sales are correlated, it is not because of some collusion between clergy and the liquor industry. It is because both are affected by how well the economy is doing.

Rule of Five There is a 93.75% chance that the median of a population is between the smallest and largest values in any random sample of five from that population.

you should start thinking about measurements as a multistep chain of thought. Inferences can be made from highly indirect observations.

Bertrand Russell once said, "Mathematics, rightly viewed, possesses not only truth, but supreme beauty. . . .

Myth: When you have a lot of uncertainty, you need a lot of data to tell you something useful. Fact: If you have a lot of uncertainty now, you don't need much data to reduce uncertainty significantly. When you have a lot of certainty already, then you need a lot of data to reduce uncertainty significantly. In other words—if you know almost nothing, almost anything will tell you something.

Instead of being overwhelmed by the apparent uncertainty in such a problem, start to ask what things about it you do know.

Measurement: A quantitatively expressed reduction of uncertainty based on one or more observations.

Two good indicators of revealed preferences are things the people tend to value a lot: time and money. If you look at how they spend their time and how they spend their money, you can infer quite a lot about their real preferences.

The fact is that the preference for ignorance over even marginal reductions in ignorance is never the moral high ground.

It is the mark of an educated mind to rest satisfied with the degree of precision which the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle (384 b.c.–322 b.c.)

The most important questions of life are indeed, for the most part, really only problems of probability. —Pierre Simon Laplace, Théorie Analytique des Probabilités, 1812

Understanding how to measure uncertainty is key to measuring risk. Understanding risk in a quantitative sense is key to understanding how to compute the value of information. Understanding the value of information tells us what to measure and about how much effort we should put into measuring it.

researchers have run experiments25 showing that experts can be trained to be better at estimating probabilities by applying a battery of estimation tests, giving the experts a lot of quick, repetitive, clear feedback along with training in techniques for improving subjective probabilities.

Any decision we think we are about to make is something that can be Googled before we commit to a choice.

It is not too bold a statement to say that a software development project is one of the riskiest investments a business makes. For example, the chance of a large software project being canceled increases with project duration. In the 1990s, those projects that exceeded two years of elapsed calendar time in development had a default rate that exceeded the worst rated junk bonds (something over 25%).

Is a programmer who gets 99% of assignments done on time and 95% error free better than one who gets only 92% done on time but with a 99% error-free rate? Is total product quality higher if the defect rate is 15% lower but customer returns are 10% higher? Is "strategic alignment" higher if the profit went up by 10% but the "total quality index" went down by 5%?

Very few experts actually measure their performance over time, and they tend to summarize their memories with anecdotes. They are right sometimes and wrong sometimes, but the anecdotes they remember tend to be more flattering to them.