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Three-point estimate

Understand how a three-point estimate works and why it helps teams discuss uncertainty instead of forcing one rigid number.

A three-point estimate uses three values to describe uncertainty: optimistic, realistic, and pessimistic.

What is a three-point estimate?

Instead of pretending that one number is enough, the team records a range of possible outcomes. That range helps show risk, complexity, and confidence more honestly.

The three values

  1. Optimistic: Best-case outcome.
  2. Realistic: Most likely outcome.
  3. Pessimistic: Worst-case outcome.

Why three-point estimates matter

  • They make uncertainty visible.
  • They improve commercial conversations.
  • They help define buffers and risk margins.
  • They are useful before committing to fixed pricing.

Example

A migration task may be estimated at 15 hours optimistic, 30 hours realistic, and 75 hours pessimistic depending on data quality and hidden rework.

How Apropo supports range-based estimating

Apropo supports range-based estimating with estimate formats that can expose minimum, average, and maximum perspectives instead of forcing one fixed number too early.

  • Estimate settings support single-value or min/max-oriented formats depending on how much certainty the team has.
  • Summary views can surface min, avg, and max perspectives across the estimate.
  • Buffers help teams reflect risk alongside the numeric estimate range.
  • Timeline views help translate estimate uncertainty into schedule thinking as well as hours and cost.

How Apropo helps refine a range-based estimate

Range estimates are most useful when teams can compare alternatives, communicate them clearly, and revisit them later.

  • Versioned estimate variants help teams compare alternative ranges without overwriting earlier assumptions.
  • Share links and export options make it easier to explain uncertainty to stakeholders in a structured way.
  • Rate settings and work types help convert estimate ranges into commercial scenarios.
  • Budget tracking can later show how the planned range compares with actual delivery data.

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