CONCEPT
Useful Imprecision
Damodaran's standard for valuation under genuine uncertainty: not the pursuit of the right answer but of an answer less wrong than the alternatives, with explicit assumptions that can be revised as evidence accumulates.
Useful imprecision is the discipline of operating with explicit, defensible, revisable estimates rather than either false precision (treating uncertain numbers as exact) or false humility (refusing to estimate at all). Damodaran's pedagogical insistence is that every valuation is wrong — the growth rate will not be what was projected, the margins will diverge from the trajectory, the discount rate will not capture the risks that actually materialize, the terminal value is a guess dressed in formula. Accepting this liberates the analyst from the pursuit of certainty and redirects effort toward the more achievable goal: being less wrong than the market and the competing analysts. The discipline requires explicit assumptions, sensitivity analysis around them, and willingness to revise when evidence changes. It is the antidote to both the spreadsheet-as-truth fallacy and the analysis-paralysis that uncertainty produces in less disciplined practitioners.
In The You On AI Field Guide
The concept is closely tied to the narrative-to-numbers bridge. Useful imprecision is what the