Confidence Intervals Beat Best and Worst Case Scenarios
Scenarios collapse probability into narratives. Intervals preserve uncertainty for decisions.
Best and worst case scenarios are comforting. They feel prudent. They are also flawed.
Scenario thinking frames uncertainty as a small set of discrete outcomes. Reality is usually continuous, not categorical. By collapsing uncertainty into a few narratives, teams lose information while creating the illusion of rigor.
The probability problem
Best and worst cases are rarely assigned meaningful likelihoods. They become rhetorical devices rather than analytical tools. Leadership hears three numbers and anchors on the middle one. The range is ignored.
Intervals preserve uncertainty
Confidence intervals force the forecaster to state not just what they expect, but how wrong they might be. That shifts the conversation from “what will happen” to “what is plausible.” It is a different posture and a better one.
Replace this box with a simple SVG or an image once you generate the chart.
Asymmetry is normal
A common failure of scenarios is false symmetry. Upside and downside risks are rarely balanced. Cost structures, capacity constraints, and demand saturation create asymmetric distributions. Scenarios often flatten these realities into clean stories that misstate risk.
Executives want a number
The usual objection is practical: leaders want a single number. Finance teams comply. This is cultural, not technical. High trust finance functions lead with distributions and conclude with decisions.
They frame choices using thresholds, not targets: what outcome forces a change in strategy, at what point risk exceeds tolerance, and how likely it is that constraints bind. Those questions cannot be answered with probability free scenarios.
Confidence intervals do not make decisions harder. They make tradeoffs explicit. They replace false certainty with usable risk awareness.
Scientific foundations: probabilistic forecasting, calibration, and evaluation of predictive distributions, including research on proper scoring rules and uncertainty communication in decision environments.
Related: chrisalbertbaker.com