Nothing is impossible to measure. We’ve measured concepts that people thought were immeasurable, like customer/employee satisfaction, brand value and customer experience, reputation risk from a data breach, the chances and impact of a famine, and even how a director or actor impacts the box office performance of a movie. If you think something is immeasurable, it’s because you’re thinking about it the wrong way. Inspirational examples of where seemingly impossible measurements were resolved with surprisingly simple methods 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” 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… First, all outliers, by definition, are rare and are "far away from the mean" (compared to the rest of the data points).

How to Measure Anything in Cybersecurity Risk | Wiley How to Measure Anything in Cybersecurity Risk | Wiley

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. Given a particular observation, it may seem more obvious to frame a measurement by asking the question “What can I conclude from this observation?” or, in probabilistic terms, “What is the probability X is true, given my observation?” But Bayes showed us that we could, instead, start with the question, “What is the probability of this observation if X were true?” Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods He also asks people to look more closely at each bound (upper and lower) on their estimated range. A 90% CI “means there is a 5% chance the true value could be greater than the upper bound, and a 5% chance it could be less than the lower bound. This means the estimators must be 95% sure that the true value is less than the upper bound. If they are not that certain, they should increase the upper bound… A similar test is applied to the lower bound.”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” Or, even easier, make use of the 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.” A measurement is an observation that quantitatively reduces uncertainty. Measurements might not yield precise, certain judgments, but they do reduce your uncertainty.

How to Measure Anything in Cybersecurity Risk How to Measure Anything in Cybersecurity Risk

I'm going to look at the total desirability of the what Adam does, at the total desirability of what Bob does... What if you want to figure out the cause of something that has many possible causes? One method is to perform a controlled experiment, and compare the outcomes of a test group to a control group. Hubbard discusses this in his book (and yes, he’s a Bayesian, and a skeptic of p-value hypothesis testing). For this summary, I’ll instead mention another method for isolating causes: regression modeling. Hubbard explains: 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. This isn’t to say that the variables you’re measuring now are “bad.” What we’re saying is that uncertainty about how “good” or “bad” a variable is (i.e. how much value they have for the predictive power of the model) is one of the biggest sources of error in a model. In other words, if you don’t know how valuable a variable is, you may be making a measurement you shouldn’t – or may be missing out on making a measurement you should. 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:

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.” Douglas Hubbard’s How to Measure Anything is one of my favorite how-to books. I hope this summary inspires you to buy the book; it’s worth it. Information can affect people’s behavior (e.g. common knowledge of germs affects sanitation behavior). To figure out which category of measurement methods are appropriate for a particular case, we must ask several questions: