Scenario Comparison Calculator
Compare up to five named scenarios with probabilities — the expected value, the best and worst cases, the spread, and how much of the expected value the single most likely scenario carries.
Expected value is each outcome times its probability, summed.
How the scenario comparison calculator works
Expected value is each outcome times its probability, summed. The probabilities must add to 100%; if they do not, they are scaled so they do, and the page says so.
The spread between best and worst is the number that decides whether the expected value is a plan or a hope. When it is many times the expected value, the average describes a future that will not happen.
Formula: expected = Σ (outcome × probability)
Worked examples
| Inputs | Expected value | Note |
|---|---|---|
| Three scenarios | 118,000 | expected 118,000 |
| A likelier downside | 68,500 | expected value falls |
| Probabilities that do not sum to 100 | 127,500 | scaled, and said so |
FAQFrequently asked questions
What is expected value?
Each outcome multiplied by its probability, added up. It is the long-run average if the situation repeated many times — which most decisions do not.
Why does the spread matter?
Because a 118,000 expectation built from −40,000 and 310,000 describes no future that will actually occur. The spread says how much the average is hiding.
What if my probabilities do not add to 100?
They are scaled so they do, and the result row says what they summed to. Relative weights are usually what people mean anyway.
How many scenarios should I use?
Three is standard — downside, base, upside. Five is the practical maximum before the probabilities become invented.
Is the most likely scenario the one to plan for?
Not necessarily. Plan for the base, but the worst case is what decides whether you can survive being wrong.
Where these figures come from
- Hubbard (2014) — How to Measure Anything — the case for ranges over point estimates
- Vose (2008) — Risk Analysis: A Quantitative Guide — Monte Carlo and the triangular distribution
- Saltelli et al. (2008) — Global Sensitivity Analysis: The Primer — one-at-a-time sensitivity and its limits
- NIST — Uncertainty analysis guidance — the US measurement standards body
Last checked: September 2026. The methods are textbook decision analysis: triangular distributions for three-point estimates, tornado ranking for sensitivity, and simple additive weighting for decision matrices.