Moar Accuracies

You've probably heard the saying, "It's better to be mostly accurate than precisely wrong." But what does that mean exactly? Aren't accuracy and precision basically the same thing?

Accuracy relates to the likelihood that outcomes fall within a prediction band or measurement tolerance. A prediction/measurement that comprehends, say, 90% of actual outcomes is more accurate than a prediction/measurement that comprehends only 30%. For example, let's say you repeatedly estimate the number of marbles in several Mason jars mostly full of marbles. An estimate of "more than 75 marbles and less than 300 marbles" is probably going to be correct more often than "more than 100 marbles but less than 120 marbles." You might say that's cheating. After all, you can always make your ranges wide enough to comprehend any range of possibilities, and that is true. But the goal of accuracy is just to be more frequently right than not (within reasonable ranges), and wider ranges accomplish that goal. As I'll show you in just a bit, accuracy is very powerful by itself.

Precision relates to the width of the prediction/measurement band relative to the mean of the prediction/measurement. A precision band that varies around a mean by +/- 50% is less precise than one that varies by +/- 10%. When people think about a precise prediction/measurement, they usually think about one that is both accurate and precise. A target pattern usually helps make a distinction between the two concepts.

The canonical target pattern explanation of accuracy and precision.

The problem is that people jump past accuracy before that attempt to be precise, thinking that the two are synonymous. Unfortunately, unrecognized biases can make precise predictions extremely inaccurate, hence the proverbial saying. Jumping ahead of the all too important step of calibrating accuracy is where the "precisely wrong" comes in.

Good accuracy trucks many more miles in most cases than precision, especially when high quality, formal data is sparse. This is because the marginal cost of improving accuracy is usually much less than the marginal costs of improved precision, but the payoff for improved accuracy is usually much greater. To understand this point, take a look again at the target diagram above. The Accurate/Not Precise score is higher than the Not Accurate/Precise score. In practice, a lot of effort is required to create a measurement situation that effectively controls for the sources of noise and contingent factors that swamp efforts to be reasonably more precise. Higher precision usually comes at the cost of tighter control, heightened attention on fine detail, or advanced competence. There are some finer nuances even here in the technical usages of the terms, but these descriptions work well enough for now.

Be careful, though - being more accurate is not just a matter of going with your gut instinct and letting that be good enough. Our gut instinct is frequently the source of the biases that make our predictions look as if we were squiffy when we made them. We usually achieve improved accuracy through the deliberative process of accounting for the causes and sources of the variation (or range of outcome) we might observe in the events we're trying to measure or predict. The ability to do this reflects the depth of expert knowledge we possess about the system we're addressing, the degree of nuances we can bring to bear to explain the causes of variation, and a recognition of the sources of bias that may affect our predictions. In fact, achieving good accuracy usually begins by assessing that we may be biased at all (and we usually are) and why.

Once we've achieved reasonable accuracy about some measurement of concern, it might then make sense to improve our precision of the measurement if the payoff is worth the cost of intensified attention and control. In other words, we only need to improve our precision when it really matters.

[Image from FreeDigitalPhotos.net by Salvatore Vuono.]

Can Modeling a Business Work?

A friend on LinkedIn asks, “Can modeling a business work?” I respond:

For now, or at least until The Singularity occurs, the development of business ideas and plans is a uniquely human enterprise that springs from a combination of intuition, goals, and ambitions. That should not mean, however, that we cannot effectively supplement our intuition and planning with aids to management and decision making. While I think human intuition is a very powerful feature of our species, I’m also convinced it can be led astray or corrupted by biases very quickly, particularly amid the complexities that arise as plans turn into real life execution. This is not a modern realization. The origin of the principles of inventory management, civil engineering, and accounting date back to the antiquities. Think of the seagoing merchants of the Phoenicians and the public works building Babylonians and Egyptians. In fact, historians now believe that the actual founder of Arthur Andersen LLP was none other than the blind Venetian mathematician and priest, Luca Pacioli (ca. 1494). That's right - that musty odor that emanates from accounting books is due to their being more than 500 years old.

Luca Pacioli doodling circles out of sheer boredom after a day of accounting. I made up the part about his being blind.

Business modeling is a tool similar to accounting in that it aids our thinking in a world whose complexity seems often to exceed the grasp of our comprehension. I look at the value of modeling a business as a means to stress test both the business plan logic and the working assumptions that drive the business plan. In regard to the business plan logic, we're asking if the business has the potential ability to produce the value we think it can; and in regard to the working assumptions, we're testing how sensitively important metrics (i.e., payback time, break-even, required resources, shareholder value) of the business plan respond to conditions in the environment and controllable settings to which our business plan will be subjected.

Obtaining such insights from modeling a business, business leaders can modify business plans by changing policies about pricing, products/services offered, costs targeted for reduction or elimination, and contingency or risk mitigation plans that can be adopted, etc. 

However, I recommend awareness of at least three caveats with regard to business modeling:

1. Think of such models as "what-ifs" more so than precise forecasts. Use the "what if" mindset to make a business plan more robust against the things outside your direct control versus using it to justify a belief in guaranteed success. The latter is almost a sure fire approach to failure. 
2. Always compare more than one plan with a model to minimize opportunity costs. Often times, the best business plans derive from hybrids of two models that show how value can be created and retained for at least two different reasons. 
3. Avoid overly complex models as much as, maybe more so than, overly simplistic models. Building a requisite model from an influence diagram first is usually the best way to achieve this happy medium before writing the first formula in a spreadsheet or simulation tool. Richer, more complex models that correspond to the real world with the highest degree of precision are usually not useful for a number of reasons:
• they can be costly to build
• the value frontier of the insights derived decline relative to the cost to achieve them as the degree of complexity increases
• they are difficult to maintain and refactor for other purposes
• they are often used to justify delaying commitment to a decision
• few people will achieve a shared understanding that is useful for collaborating and execution

A requisite model, on the other hand, should deliver clarity and permit making new and interesting testable predictions or reveal insights about, say, uncertainties, that could be made to work in your favor. Admittedly, though, it takes a lot of practice to achieve this third recommendation, but it should be used as a guiding principle.

Do Risk Analysts Dream of Electron Microscopes?

From as early as I can remember, I have always wanted to be a scientist. Indeed, while most kids my age were doing normal, healthy kid things on summer afternoons, like engaging in war games or playing with anatomically disproportionate Barbie dolls, I was usually in my secret lab (which was actually a sewing table my father converted to a “lab bench”) looking through my Bendai microscope or mixing chemicals with my Science Fair Chemcraft chemistry set. I distinctly remember on the playground one day, after being bowled over in a dodge ball game, one of my grade school classmates asking me, “You don’t really like sports, do you?” I responded through bloodied lips, “I like to think of science as my sport.” Of course, that admission advanced my standing in the picking order for the next game, as the best were always saved for last.

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