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The purpose of this book is to show organizations two things: 1. Intangibles that appear to be completely intractable can be measured. 2. This measurement can be done in a way that is economically justified.
Let me begin by stating the three propositions as a way to define and approach the problem of measurement in business: 1. Management cares about measurements because measurements inform uncertain decisions. 2. For any decision or set of decisions, there are a large combination of things to measure and ways to measure them—but perfect certainty is rarely a realistic option. 3. Therefore, management needs a method to analyze options for reducing uncertainty about decisions.
Measurement is about supporting decisions, and there are even several decisions to make within measurements themselves.
Nobody should care about measuring something if it doesn’t inform a significant bet of some kind.
career. He was famous for teaching his students skills to approximate fanciful-sounding quantities that, at first glance, they might presume they knew nothing about. The best-known example of such a “Fermi question” was Fermi asking his students to estimate the number of piano tuners in Chicago.
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.
Rule of 5, 93.75% chance of the median will be between the largest and smallest of 5 random samples
An important lesson comes from the origin of the word experiment. “Experiment” comes from the Latin ex-, meaning “of/from,” and periri, meaning “try/attempt.” It means, in other words, to get something by trying. The statistician David Moore, the 1998 president of the American Statistical Association, goes so far as to say: “If you don’t know what to measure, measure anyway. You’ll learn what to measure.”3 We might call Moore’s approach the Nike method: the “Just do it” school of thought.
Four Useful Measurement Assumptions 1. Your problem is not as unique as you think. 2. You have more data than you think. 3. You need less data than you think. 4. An adequate amount of new data is more accessible than you think.
It’s been done before. No matter how difficult or “unique” your measurement problem seems to you, assume it has been done already by someone else, perhaps in another field. If this assumption turns out not to be true, then take comfort in knowing that you might have a shot at a Nobel Prize for the discovery. Seriously,
1. Define a decision problem and the relevant uncertainties. If people ask “How do we measure X?” they may already be putting the cart before the horse. The first question is “What is your dilemma?” Then we can define all of the variables relevant to the dilemma
and determine what we really mean by ambiguous ideas like “training quality” or “economic opportunity.” (Chapter 4) 2. Determine what you know now. We need to quantify your uncertainty about unknown quantities in the identified decision. This is done by learning how to describe your uncertainty in terms of ranges and probabilities. (This is a teachable skill.) Defining the relevant decision and how much uncertainty we have about it helps us determine the risk involved. (Chapters 5 and 6) 3. Compute the value of additional information. Information has value because it reduces risk in decisions.
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Repeat step 3. 5. Make a decision and act on it. When the economically justifiable amount of uncertainty has been removed, decision makers face a risk-versus-return decision. Any remaining uncertainty is part of this choice. To optimize this decision, the risk aversion of the decision maker can be quantified. An optimum choice can be calculated even in situations where there are enormous combinations of possible strategies. We will build on these methods further with a discussion about quantifying risk aversion and other preferences and attitudes of decision makers. This and all of the
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The “how much?” question frames any issue in a valuable way, and even the most controversial issues of measurement in business, government, or private life can be addressed when the consequences of not measuring are understood.
Confronted with apparently difficult measurements, it helps to put the proposed measurement in context. Prior to making a measurement, we need to answer the following: 1. What is the decision this measurement is supposed to support? 2. What is the definition of the thing being measured in terms of observable consequences? 3. How, exactly, does this thing matter to the decision being asked? 4. How much do you know about it now (i.e., what is your current level of uncertainty)? 5. What is the value of additional information? In this chapter, we will focus on the first three questions.
If a measurement matters at all, it is because it must have some conceivable effect on decisions and behavior. 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.
in arcane
devices were simple and obvious. Nor were they, like some managers today, dismissive of instruments because they had limitations and errors of their own. Of course they have errors. The question is “Compared to what?” Compared to the unaided human? Compared to no attempt at measurement at all? Keep the purpose of measurement in mind: uncertainty reduction, not necessarily uncertainty elimination.
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.)
from the supplementary Web site, www.howtomeasureanything.com. Make full use of that resource.
and road conditions. The second highest information value was
