Concept
False precision is presenting a number with more apparent exactness — more significant figures, a tighter range, finer resolution — than the method and inputs that produced it can support. It misrepresents an order-of-magnitude estimate as an exact one, and is among the most common ways an early-stage analysis loses credibility.
What it is. Every reported figure carries an implied precision: “$805.43/t” implies the analysis can tell $805 from $806; ”~$800/t ±30%” implies it cannot. False precision is any presentation whose implied precision exceeds the estimate’s true accuracy class — a property of how a number is written and shown, not of whether the number is right.
Where it shows up. Excess significant figures (a cost to the cent off inputs known only to ±tens of percent); over-tight ranges (a band narrower than the inputs justify); a bare point estimate (a single figure with no band, which implies zero uncertainty); and over-resolution (a cost broken into many line items, or a chart drawn finer than the data, implying each part is independently pinned down).
Why it happens, and precision ≠ accuracy. Arithmetic preserves digits: multiply a few rounded anchors and the spreadsheet returns ten figures, none earned — precision is bounded by the weakest input, not the cleanliness of the calculation. A number can be accurate (close to the truth) yet falsely precise (written as though exact), and imprecise yet honest; the two axes are independent. The honest presentation matches displayed resolution to real resolution — usually one or two significant figures with an explicit band.