Understanding precision and accuracy through a Boeing 747 wingspan example

Precision, accuracy, and why they matter in FAIR-style thinking

Let me tell you a quick story. Someone says the wingspan of a Boeing 747 is exactly 33.2 meters. Sounds specific, right? But is it true? Not quite. The real wingspan is about 68.4 meters. So the number feels precise—it’s a clean, detailed figure—but it isn’t accurate. It points to a bigger truth that shows up in risk work, too: precision and accuracy aren’t the same thing, and mixing them up can tilt decisions the wrong way.

What precision and accuracy really mean

Think of precision as the level of detail in a measurement. If you measure something many times and keep getting the same number, you’re being precise. It’s about consistency and repeatability. Accuracy, on the other hand, asks how close your result is to the true value. You can be precise and wrong at the same time, or you can be accurate without a lot of detail if your measurements are broad but correct on average.

In the wingspan example, 33.2 meters is highly precise: it’s a neat, exact figure. But since the true wingspan is around 68.4 meters, that 33.2 is not accurate. The gap isn’t random noise—it’s a fundamental misalignment with reality.

How this shows up in FAIR-style risk thinking

FAIR — the framework for information risk — is all about turning uncertainty into something you can manage. You quantify the likelihood of events, the potential loss, and the overall risk. When you feed that model, the inputs matter a lot. If an input is precise but not accurate, your risk picture can look rock-solid but be misaligned with what’s actually happening.

Examples you might run into:

• Asset value estimates. If you wean a price into a model with a tight, exact number but that price is biased (say, you used an outdated market snapshot), the result looks crisp but is off the mark.

• Likelihood of a breach. You might have a precise count of incidents from a small sample. If that sample isn’t representative, the number is precise but not accurate—it undercuts the real risk level you face.

• Impact estimates. A dollar figure attached to a loss scenario can be precise, yet if you misjudge the underlying damage (e.g., reputational harm or regulatory fines), the precision won’t save you.

In short, precision helps you express your thinking clearly, but accuracy keeps you honest about what you’re really measuring.

Why accuracy matters for decision making

Here’s the thing: risk decisions are often about choosing between options under uncertainty. If your inputs are precise but biased, you’ll be confident about the wrong conclusion. You’ll act as if you’ve got sharper eyes than you actually do. If your inputs are accurate but fuzzy, your results might feel shaky, but they won’t mislead you into overconfidence. The best situations blend both: you want precise inputs, but those inputs should be anchored to reality.

A helpful analogy: a weather forecast. Forecasters often present probability bands. The exact number for tomorrow’s temperature is less important than whether the forecast captures the range where the true value sits. The same logic applies to risk: give stakeholders a clear, honest sense of both where your estimates are precise and how close they are to the truth.

How to improve both precision and accuracy in your FAIR work

You don’t have to choose one over the other. Here are practical moves to keep both in check:

• Use multiple data sources. If you pull from several datasets, you’ll see where numbers agree (that’s good precision) and where they diverge (a flag for accuracy questions). Diversified inputs tend to calm overconfidence.

• Calibrate against knowns. Compare your inputs to well-established benchmarks. If your estimates consistently miss the mark, revisit the method instead of forcing a prettier number.

• State assumptions openly. If you assume a constant threat rate or a fixed replacement cost, say so. Clear assumptions help you gauge how much your precision is worth and where accuracy might suffer.

• Use ranges and intervals. Instead of a single point, show a plausible interval. Acknowledge the uncertainty, and connect interval width to data quality and model complexity.

• Run sensitivity analyses. Ask: which inputs move the result the most? When you know the “drivers,” you can focus on improving the most influential numbers, raising both precision and accuracy where it matters.

• Document the method end-to-end. Record how you arrived at each number, what constraints you used, and what would change if inputs shift. That transparency itself buys accuracy, because it invites scrutiny and improvement.

• Validate with real outcomes. When possible, check model outputs against actual events. Validation isn’t a one-and-done step; it’s a habit that helps keep results honest over time.

A few practical tips you can use today

• Build a simple, convincing narrative around the data. People trust stories that feel grounded. A clear narrative helps you justify why a number is precise and why you believe it’s close to reality.

• Present uncertainty plainly. Don’t pretend you have a perfect answer. Acknowledge the confidence you have in the numbers and where that confidence comes from.

• Use visual aids. Gentle charts showing ranges, not just points, can make the difference between a confusing deluge of numbers and a clear, actionable view.

• Be mindful of biases. If you know a source tends to overstate outcomes, factor that bias into your accuracy assessment and adjust accordingly.

• Keep it human. Data is important, but people decide with imperfect information. Make your explanation relatable, with real-world stakes and simple language.

A little digression that stays on track

If you’ve ever watched a sports replay with a camera that crops in tightly, you’ve seen this idea in action. A zoomed-in view can feel incredibly precise—you can see every stitch, every grip. Yet the true game outcome isn’t decided by a single frame. You need the full context, and sometimes the zoomed view is misrepresentative of the bigger picture. In risk work, a single precise number is tempting, but you’ll do better by pairing it with the bigger context—what the data says about the real world and what your model actually captures.

Bringing it back to the core takeaway

Precision gives you clarity and repeatability. Accuracy keeps you honest about how close your results are to what’s real. In the realm of information risk, both matter. A precise, well-documented input can sharpen your understanding, but if that input isn’t anchored to reality, the sharpened edge cuts the wrong way.

So, as you map out risk scenarios, remember the wingspan lesson: a number can be precise and wrong. Don’t settle for crisp figures that drift away from the truth. Build inputs you can trust, report the uncertainties clearly, and let your analysis ride on a foundation that’s both tight in detail and faithful to reality.

If you want a quick mental checklist for your next risk assessment, here’s a compact version:

• Does every input come from more than one source?

• Have I stated my assumptions and checked how they influence results?

• Have I provided a confidence interval or a scenario range?

• Do I know which inputs most affect the outcome, and have I reviewed those first?

• Is there a plan to validate the outputs against real-world data when possible?

Take a breath, map your inputs, and let the numbers do their job without pretending they’re more certain than they are. That balance—clarity plus honesty—will serve you well as you explore the FAIR framework and the challenges of information risk.