Cone of Uncertainty
The Cone of Uncertainty is a model of estimation variance over the lifecycle of a software project, first articulated by Barry Boehm in his 1981 book Software Engineering Economics. Estimates made at project inception have roughly 4× variance (a project budgeted at 100 staff-weeks may take 25 to 400); the cone narrows to 1× (the actual outcome) as the project progresses through requirements, design, coding, and acceptance testing.
The cone is descriptive — what estimation accuracy looks like in practice across a wide range of projects — not prescriptive. It does not say 'estimates must improve as the project proceeds' but rather 'across observed projects, they do, because uncertainty falls as decisions get locked in.' Boehm revisited the cone in 2002 with co-author Richard Turner, broadly replicating the shape on more recent projects. Modern Agile pushback: the cone assumes a single project trajectory with progressive lock-in; sprints introduce repeated cones at smaller scale. Either way, the cone's most useful implication is that early estimates are wide bands, not point predictions — and committing to a point inside the wide band is the planning anti-pattern that the planning fallacy explains.
Related terms
- Planning fallacy
The planning fallacy is the cognitive bias toward predicting that one's own task will take less time than past tasks of similar scope.
- Calibration error
Calibration error is the gap between the confidence someone reports in a prediction and the empirical accuracy of those predictions.
- Story points
A story-point estimate is a unit-less measure of relative effort assigned to a user story.