A detailed plan can still be badly wrong when its persuasive internal story is mistaken for evidence about what will happen. To test it, compare its assumptions with the actual results of similar completed plans, track past forecast errors, and check whether the decision still works across a range of plausible outcomes.
Why can a plan sound convincing and still be wrong?
A plan’s logic explains how its authors think events might unfold; it does not show how similar plans have actually performed. When decision makers treat a proposal as unique, they may anchor forecasts on its own scenarios and overlook the statistical record of past outcomes.
In their 1993 paper, Daniel Kahneman and Dan Lovallo describe this as the “inside view”: “Overly optimistic forecasts result from the adoption of an inside view of the problem, which anchors predictions on plans and scenarios.” The paper explains why a coherent case can produce an overconfident forecast, not why every detailed plan is wrong. Read the paper in Management Science.
What is the outside view?
The outside view starts with what happened to a relevant group of completed, comparable cases. Reference-class forecasting uses those actual outcomes to establish a forecast range, then asks whether there is evidence that the current proposal should differ from that comparison.
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For example, a project team estimating cost and schedule can look at final costs and completion times for similar completed projects—not only their original budgets and timetables. The comparison does not predict the future with certainty; it provides an empirical check on a forecast built from the proposal’s own assumptions. Homes England describes the application of optimism-bias and contingency methods to project cost estimates in its public-sector work. See its accessible report.
How to test a consequential plan
- Define the comparison class. Identify which completed cases are similar enough to inform this forecast, and explain why. Keep the class visible rather than selecting examples only because they support the preferred plan.
- Use actual outcomes. Compare what happened with the original forecasts for cost, duration, and benefits where those data are available. An archive of completed outcomes is more useful than a collection of plans that were never checked against results.
- Measure forecast errors. Look for the size and consistency of the difference between estimates and outcomes. HM Treasury’s 2026 Green Book says appraisal adjustments should draw on an organisation’s historical forecast errors and, where possible, evidence from similar proposals. Read the Green Book.
- Explain proposed adjustments. If the current case differs from the comparison group, name the relevant differences and show how they affect the estimate. Avoid adjustments that cannot be tested against evidence or outcomes.
- Stress-test the decision. Check what happens if costs rise, delivery takes longer, or benefits are lower than expected. Ask whether the decision remains acceptable under a worse but plausible outcome, rather than treating one point estimate as a guarantee.
What optimism-bias adjustments can—and cannot—do
HM Treasury defines optimism bias in its appraisal guidance as “the demonstrated systematic tendency for practitioners to be over-optimistic about key assumptions in appraisal, such as social costs, social benefits or project duration.” In that appraisal context, the guidance calls for increasing estimated costs and timeframes and decreasing estimated benefits to account for the bias.
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The Green Book says adjustments should be informed by historical forecast errors and comparable proposals when possible. Separate supplementary guidance provides generic adjustments for cases where more robust primary data is unavailable; those figures are not universal uplifts for every plan or project. Read HM Treasury’s optimism-bias supplementary guidance.
These are UK central-government appraisal sources, not instructions that automatically apply unchanged to personal plans or every business decision. A numerical adjustment can make an assumption explicit, but it cannot eliminate uncertainty or guarantee a correct forecast.
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How to handle uncertainty without false precision
When evidence is weak, a precise probability can make an uncertain judgment look more certain than it is. The 2026 Green Book notes that real-options analysis may require probabilities for scenarios and can introduce spurious accuracy. Where probabilities are poorly supported, use ranges and stress tests, and state what evidence would change the assessment.
Keep the comparison class and any adjustments visible. A reference class can be chosen to favor a desired conclusion, while case-specific reasons for departing from it can be difficult to disprove. These limitations make transparent choices and recorded outcomes important; they do not make historical comparisons useless. Vista Research discusses challenges in applying reference classes.
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What a useful forecast record should show
For decisions with meaningful cost, schedule, or benefit assumptions, record the comparison and the forecast so the eventual result can be checked. The right measures depend on the decision; these are practical questions, not a universal official checklist.
- Which completed cases were treated as comparable, and why?
- What were their actual outcomes, and how did those differ from their forecasts?
- What evidence supports any adjustment for differences between those cases and this plan?
- What plausible adverse outcome was tested, and would the decision still be acceptable?
- For alternatives, how do cost, benefit, duration, forecast error, and uncertainty compare—and does the preferred option change under stress?
Revisiting these records after delivery or completion closes the loop: future plans can be checked against actual performance instead of relying on confidence alone.
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