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

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Systemic error is also called a “bias.” Based on his experience, Hubbard suspects the three most important to avoid are: And of course programming is diverse enough to encompass a wide variety of needs and skillsets. Say, programmer A is great at writing small self-contained useful libraries, programmer B has the ability to refactor a mess of spaghetti code into something that's clear and coherent, programmer C writes weird chunks of code that look strange but consume noticeably less resources, programmer D is a wizard at databases, programmer E is clueless about databases but really groks Windows GUI APIs, etc. etc. How are you going to compare their productivity? For example] if, hypothetically, we know that only 20% of the population will continue to shop at our store, then we can determine the chance [that] exactly 15 out of 20 would say so… [The details are explained in the book.] Then we can invert the problem with Bayes’ theorem to compute the chance that only 20% of the population will continue to shop there given [that] 15 out of 20 said so in a random sample. We would find that chance to be very nearly zero… Decision optimization: The final business decision recommendation is made (this is rarely a simple “yes/no” answer).

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

How computing the value of information will show that you probably have been measuring all the wrong things Determine what you know. (Quantify your uncertainty about those variables in terms of ranges and probabilities.) Adds even more intuitive explanations of powerful measurement methods and shows how they can be applied to areas such as risk management and customer satisfaction For many decisions, one decision is required if a value is above some threshold, and another decision is required if that value is below the threshold. For such decisions, you don’t care as much about a measurement that reduces uncertainty in general as you do about a measurement that tells you which decision to make based on the threshold. Hubbard gives an example:Another example: I took statistics on how my friends played games that involved bidding, such as Liar's Poker. I found that they typically would bid too much. Therefore a measurement of how many times someone had the winning bid was a high predictor of how they would perform in the game-people who bid high would typically lose. Hubbard says a few things in support of this approach. First, he points to some studies (e.g. El-Gamal & Grether (1995)) showing that people often reason in roughly-Bayesian ways. Next, he says that in his experience, people become better intuitive Bayesians when they (1) are made aware of the base rate fallacy, and when they (2) are better calibrated.

Measuring - BBC Teach Measuring - BBC Teach

In fact, the more valuable predictive factor was whether or not the combat vehicle had been in a specific area before . It turns out that vehicle commanders, when maneuvering in an uncertain area (i.e. landmarks, routes, and conditions in that area they had never encountered before), tend to keep their engines running for a variety of reasons. That burns fuel. We must also distinguish precision and accuracy. A “precise” measurement tool has low random error. E.g. if a bathroom scale gives the exact same displayed weight every time we set a particular book on it, then the scale has high precision. An “accurate” measurement tool has low systemic error. The bathroom scale, while precise, might be inaccurate if the weight displayed is systemically biased in one direction – say, eight pounds too heavy. A measurement tool can also have low precision but good accuracy, if it gives inconsistent measurements but they average to the true value.

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Adds recent research, especially in regards to methods that seem like measurement, but are in fact a kind of “placebo effect” for management – and explains how to tell effective methods from management mythology Once you determine what you know about the uncertainties involved, how can you use that information to determine what you know about the risks involved? Hubbard summarizes: 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. The last step will make more sense if we first “bring the pieces together.” Hubbard now organizes his consulting work with a firm into 3 phases, so let’s review what we’ve learned in the context of his 3 phases. Updated decision model: The AIE analyst updates the decision model based on the results of the measurements.

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

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. Suppose you enter this formula on cell A1 in Excel. To generate (say) 10,000 values for the maintenance savings value, just (1) copy the contents of cell A1, (2) enter “A1:A10000” in the cell range field to select cells A1 through A10000, and (3) paste the formula into all those cells. Amount of material produced (in propositions or subsections of proof) that, if correct, will actually be part of my answer in the end. 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.If it’s really that important, it’s something you can define. If it’s something you think exists at all, it’s something you’ve already observed somehow. If it’s something important and something uncertain, you have a cost of being wrong and a chance of being wrong. In other words, the quantitative method you use to make measurements and decisions only has to beat the alternative. Any empirical method you incorporate into your process can improve it if it provides more practical and accurate insight than what you were doing before.