How to Measure Anything: Finding the Value of Intangibles in Business

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That seems to be a better fit for the impression of progress. You wouldn't tend, in retrospect, to call it progress if you realised you'd been going in completely the wrong direction.

How to Measure Anything | Wiley Online Books

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)” When measuring risk, we don’t just want to know the “average” risk or benefit. We want to know the probability of a huge loss, the probability of a small loss, the probability of a huge savings, and so on. That’s what Monte Carlo can tell us. Why do we care about measurements at all? There are just three reasons. The first reason—and the focus of this book—is that we should care about a measurement because it informs key decisions. Second, a measurement might also be taken because it has its own market value (e.g., results of a consumer survey) and could be sold to other parties for a profit. Third, perhaps a measurement is simply meant to entertain or satisfy a curiosity (e.g., academic research about the evolution of clay pottery). But the methods we discuss in this decision-focused approach to measurement should be useful on those occasions, too. If a measurement is not informing your decisions, it could still be informing the decisions of others who are willing to pay for the information.”

For example, to determine how much time sales reps spend in meetings with clients versus other administrative tasks, they might choose a complete review of all time sheets… [But] if a complete review of 5,000 time sheets… tells us that sales reps spend 34% of their time in direct communication with customers, we still don’t know how far from the truth it might be. Still, this “exact” number seems reassuring to many managers. Now, suppose a sample of direct observations of randomly chosen sales reps at random points in time finds that sales reps were in client meetings or on client phone calls only 13 out of 100 of those instances. (We can compute this without interrupting a meeting by asking as soon as the rep is available.) As we will see [later], in the latter case, we can statistically compute a 90% CI to be 7.5% to 18.5%. Even though this random sampling approach gives us only a range, we should prefer its findings to the census audit of time sheets. The census… gives us an exact number, but we have no way to know by how much and in which direction the time sheets err. After following the above steps, Hubbard writes, “the measurement instrument should be almost completely formed in your mind.” But if you still can’t come up with a way to measure the target variable, here are some additional tips: Uh, pretty accurately. Object selection is a critical feature; the entire functionality of the app depends on it. The usefulness of not having your data be corrupted is also obvious. I'm not really sure what you mean by asking whether I know in advance how useful a feature or bug fix will be. Of course I know. How could I not know? I always know.

7 Simple Principles for Measuring Anything - Hubbard Decision

Unfortunately, few people are well-calibrated estimators. For example in some studies, the true value lay in subjects’ 90% CIs only 50% of the time! These subjects were overconfident. For a well-calibrated estimator, the true value will lie in her 90% CI roughly 90% of the time. Can the thing be forced to occur under new conditions which allow you to observe it more easily? E.g. you could implement a proposed returned-items policy in some stores but not others and compare the outcomes. Make a decision and act on it. (When you’ve done as much uncertainty reduction as is economically justified, it’s time to act!) Preliminary measurement method designs: Focusing on the few variables with highest information value, the AIE analyst chooses measurement methods that should reduce uncertainty.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”

How to Measure Anything: Finding the Value of Intangibles in

What’s the really simple question that makes the rest of the measurement moot? First see if you can detect any change in research quality before trying to measure it more comprehensively.This cost should be internalised by the murderer, but many people seem ignorant of the cost, leading to an over-supply of murder. It'd be good to know how big the market failure is (so we can judge various preventative policies). The fact is that the preference for ignorance over even marginal reductions in ignorance is never the moral high ground.” Initial research: Interviews and secondary research to get familiar on the nature of the decision problem.

How to Measure Anything: Finding the Value of Explaining ‘How to Measure Anything: Finding the Value of

In most cases, we’ll estimate the values in a population by measuring the values in a small sample from that population. And for reasons discussed in chapter 7, a very small sample can often offer large reductions in uncertainty. Use search terms often associated with quantitative data. E.g. don’t just search for “software quality” or “customer perception” – add terms like “table,” “survey,” “control group,” and “standard deviation.” It concerns me. Because business is already full of dubious metrics that actually do harm. For instance, in programming, source lines of code (SLOC) per month is one metric that is used to gauge 'programmer productivity', but has come under extreme and rightful skepticism. I fixed it. Now, the data does not get corrupted when the user takes certain actions. Before, it did. The Rule of Five has another advantage over the t-statistic: it works for any distribution of values in the population, including ones with slow convergence or no convergence at all! It can do this because it gives us a confidence interval for the median rather than the mean, and it’s the mean that is far more affected by outliers.

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Continues to boldly assert that any perception of “immeasurability” is based on certain popular misconceptions about measurement and measurement methods