Perfect precision with zero utility is worthless. Perfect utility with zero precision is dangerous. Pragmatic rigor finds the exactness sweet spot where increased precision actually changes decisions. Beyond that point, more rigor is waste. Before that point, insufficient rigor is recklessness.
Most analysis treats precision as inherent virtue. “We need more data.” “Let’s get the exact number.” These statements sound responsible until you ask: Will additional precision change what we do?
Often the answer is no. You’re debating whether market size is $47B or $52B when the strategic decision depends on whether it’s growing or shrinking. You’re calculating retention to three decimal places when you lack statistical significance at one decimal.
The opposite failure is equally common: insufficient precision where it matters. Making capital allocation based on “seems like a good opportunity.” Both failures stem from the same error: not calibrating precision to decision point.
The Three Precision Calibrations
Calibration 1: Directional vs. Magnitude – Many decisions require only direction, not precise magnitude. Is this metric improving or degrading? If “material” means more than 5% impact, you need precision to distinguish 4% from 6%, but not 12% from 14%. Directional precision suffices when you can confidently place the outcome on one side of the threshold.
Calibration 2: Order of Magnitude vs. Exact Value – Is this a million-dollar opportunity or ten-million-dollar? Initiative A returns $10 for every $1 invested, Initiative C returns $0.50. Order-of-magnitude suffices for filtering. Deploy precision only for close calls.
Calibration 3: Point Estimate vs. Range vs. Distribution – Different decisions require different precision forms. Point estimate works when decision is insensitive to variance. Range becomes necessary when variance affects decision. Full distribution matters for irreversible decisions with asymmetric outcomes.
The Common Precision Traps
False Precision Through Calculation: Taking uncertain inputs and calculating precise outputs creates illusion of precision while actual uncertainty remains massive.
Precision Theater: Conducting rigorous analysis of non-critical variables while ignoring critical uncertainty. The appearance of rigor masks actual recklessness.
Precision Procrastination: Using “need more data” as decision avoidance. There’s always more analysis possible – the question is whether additional data changes the decision.
Key Takeaway
The calibration question: What form of precision does the decision actually require? Target research at uncertainty that matters – not everything, just what’s both uncertain and decision-relevant. You might resolve 80% of decisions with 20% of research effort.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
