The Point Estimate Is a Lie of Format

Confidence: hypothesis. The forecasting theory below is drawn from published statistical literature on probabilistic prediction, not firsthand deployment. The reporting standard is an argument I have not shipped at scale. Argue with it.

“14 patients per month.” “Ships in six weeks.” “92% likely to close.” Every one of these numbers looks precise. Almost none of them are honest, because the precision is doing work the underlying estimate never actually did. It's presenting a distribution of plausible outcomes as if it were a single measured fact.

The clinical why

A clinical trial's enrollment forecast is a genuinely uncertain quantity: a function of epidemiology, screening rates, consent rates, and competing-trial pressure, each one itself an estimate. Multiply several uncertain factors together and the honest output is a range of plausible enrollment rates, wider than anyone wants to put in a budget deck. What actually gets put in the deck, traditionally, is one number in the middle, with the uncertainty quietly deleted before the slide gets built.

That deletion isn't a rounding convenience. It's the single most consequential edit in the whole forecasting process, because it's the moment a distribution of honest possibilities becomes a promise nobody can defend when the real number lands somewhere else in that distribution, which, statistically, it usually does, since a distribution's mean is rarely its most likely single outcome in the way a bare number implies.