Enter an optimistic, most likely, and pessimistic estimate for each task. We apply the PERT formula to compute the expected value, standard deviation, and the confidence levels you can actually commit to — across your whole task list, not just one task.
| Task (optional) | Optimistic (O) | Most likely (M) | Pessimistic (P) | PERT | σ | |
|---|---|---|---|---|---|---|
| — | — |
A single number is not an estimate — it's a guess with the uncertainty amputated. Percentile estimates state the same forecast at different confidence levels, so you can choose how much risk to carry.
The PERT expected value itself. Half of your projects will finish before this number, half after. Quoting P50 to a client means a 50% chance of being late — fine internally, risky as a commitment.
You'll beat this estimate 85% of the time. This is the number most experienced delivery teams put in fixed-price proposals: high enough to absorb normal turbulence, not so padded that you lose the bid.
Miss this only once in twenty projects. Use it for hard deadlines with contractual penalties, or when the downside of slipping is much worse than the cost of the buffer.
For a deeper look at picking the right confidence level for client commitments, read our guide on why the 85th percentile is the estimate you should commit to.
PERT — the Program Evaluation and Review Technique — turns three honest guesses into a statistically usable estimate. Instead of asking “how long will this take?”, you ask for an optimistic (O), most likely (M), and pessimistic (P) value, then weight them:
The result is an expected value that accounts for risk, and a standard deviation that tells you how much to trust it.
A weighted average of the three points. The most-likely estimate counts four times because real outcomes cluster around it — the beta distribution behind PERT is peaked, not flat.
The spread of a task's risk. A task estimated at 4–6–8 hours is far more predictable than one at 2–6–20, even though both have the same most-likely value.
Standard deviations combine by root-sum-of-squares, not addition. Independent risks partially cancel out — which is why a project's realistic worst case is much better than the sum of every task's worst case.
PERT approximates a beta distribution — a bell-like curve stretched between your optimistic and pessimistic bounds. Most real durations land near the peak, so the peak gets four of the six weights. The simple triangular average (O + M + P) / 3 treats a one-in-a-hundred disaster as seriously as the everyday case, which systematically inflates estimates. PERT keeps the tail risk in the standard deviation instead, where it belongs.
You can reproduce everything on this page in a spreadsheet. Put your optimistic, most likely and pessimistic estimates in columns B, C and D, then add the formulas on the right.
The catch: the spreadsheet goes stale the moment scope changes. If your task list already lives in Excel, you can import it straight into Axioplan and get live PERT estimates that update as the plan moves.
Common questions about PERT and three-point estimation.