Explore the uncertainty
behind an estimate.
Choose a best-case estimate, a worst-case estimate, and a likelihood phrase. Explore survey-based distributions or compare Kent’s proposed probability scale.
Loading the probability data…
How the estimates are distributed
Based on probability-perception survey work, rather than an arbitrary curve. Read the source and background.
Samples inside the selected interval · pale
bars show the remaining samples. The faint curve is a visual guide.
Expected Value
Compare with Sherman Kent’s probability scale
In Words of Estimative Probability (1964), Kent proposed numerical meanings for a small set of likelihood expressions. These are reference probabilities with approximate ranges, rather than sampled distributions.
| Expression | Reference probability | Approximate range |
|---|---|---|
| Certain | 100% | 100% |
| Almost certain | 93% | 87–99% |
| Probable | 75% | 63–87% |
| Chances about even | 50% | 40–60% |
| Probably not | 30% | 20–40% |
| Almost certainly not | 7% | 2–12% |
| Impossible | 0% | 0% |
Kent’s original ranges are not confidence intervals and do not specify a distribution. In Kent model mode, we assume a triangular shape with these endpoints and the reference probability as its mode. This added shape is our assumption, not a result of Kent’s work. Certain and Impossible use point masses.
Research background and model assumptions
This experiment builds on zonination’s Perceptions of Probability and Numbers, which reports survey responses from Reddit’s r/SampleSize community. Respondents assigned numerical probabilities to phrases such as “Almost Certainly” and “About Even.” The survey was inspired by Sherman Kent’s work on communicating uncertainty; it is not the original CIA study.
The research provides the motivation for using phrase-specific distributions. Mapping those distributions between your optimistic and pessimistic estimates is this project’s modeling choice, rather than a finding of the survey.
Survey mode maps sampled interpretations of probability phrases onto your estimate range. Kent mode uses a constructed triangular distribution, discretized into one-percentage-point bins by integrating its probability mass. Both sources are mapped onto your optimistic and pessimistic estimates. It treats the CSV percentages as discrete weights, normalizes rounding differences, and calculates equal-tail quantiles.
This is an exploratory mapping, not a measured forecast of task completion. The selected coverage describes the model, not a guarantee about your work. Because the samples are discrete, the interval can contain more mass than the selected coverage. Values are shown to two decimal places.