Estimating uncertain quantities—from project timelines to the impact of interventions—is crucial in fields like philanthropy, engineering, and policy analysis. However, current tools often require coding skills, lack intuitive interfaces, and aren’t optimized for collaboration or mobile use. This gap leads to slower, less accurate estimates or even avoidance of quantitative methods, undermining decision-making in high-stakes areas.

Intuitive Estimation for Everyone

One way to bridge this gap is by creating a tool that blends the flexibility of probabilistic programming with the accessibility of no-code interfaces. For example:

• Users could define distributions by adjusting sliders or sketching curves instead of writing code.
• A mobile app would enable quick estimates on the go, syncing with a desktop version for deeper analysis.
• Interactive tutorials would teach estimation concepts (e.g., lognormal distributions) while users work.

Advanced features might include scripting for complex models and collaboration tools for teams to annotate assumptions or track revisions. This approach could appeal to non-technical professionals, educators teaching statistics, and organizations needing auditable estimates.

Fitting Into the Existing Landscape

Current tools like Squiggle (powerful but code-heavy) or Guesstimate (spreadsheet-like but limited) leave room for improvement. A new tool could retain Squiggle’s flexibility while adding no-code usability, or expand Guesstimate’s approach with mobile support and richer modeling. Unlike AI-assisted tools like Elicit, the focus would be standalone estimation with deeper functionality.

Testing the Waters

An MVP could start with a web-based prototype offering no-code distribution building atop an existing engine like Squiggle. Early testing with both technical and non-technical users could validate key assumptions, such as whether mobile use justifies development or if collaboration features improve estimate quality. Later stages might introduce open-source components for community contributions while exploring sustainability via freemium tiers or sponsored development.

By prioritizing usability without sacrificing power, this approach could make quantitative estimation more accessible and impactful across disciplines.