Anchor on the base rate before adding inside-view details
Start your forecast from the class median, then adjust — do not start from your narrative and adjust to the base rate.
Why it works
Anchoring research shows that adjustments from an initial number are insufficient — people consistently end up closer to their starting point than the evidence warrants. If you start from the inside view (which is optimistic) and try to adjust downward toward the base rate, you will not adjust far enough. Starting from the base rate and adjusting upward for genuine distinguishing features of your project produces much more accurate final estimates.
How to do it
- Set the base-rate median as your initial estimate.
- List only specific, verifiable features that make your project genuinely different from the class average.
- For each feature, estimate the directional and magnitude adjustment it warrants — and be conservative.
- Treat the final estimate as a point on a distribution, not a point, and communicate the range.
Evidence
Anchoring and insufficient adjustment is one of the most replicated findings in judgment research (Tversky & Kahneman). Applied to forecasting, it predicts that base-rate anchoring will outperform inside-view anchoring with adjustment, which is what Flyvbjerg’s empirical work on projects confirms. (observational)
In domains where the base rate is poorly estimated or the population is genuinely heterogeneous, base-rate anchoring can be as misleading as inside-view anchoring.
Sources
- Tversky & Kahneman (1974), "Judgment under uncertainty: Heuristics and biases," Science
- Flyvbjerg (2008), "Curbing optimism bias and strategic misrepresentation," European Planning Studies
Common mistake
Using the base rate only as a sanity check at the end rather than as the starting anchor — at that point the inside-view estimate is already committed and the base rate is rationalized away.
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More practices for Reference Class Forecasting
- Identify the right reference class for your situation
Find a well-defined set of past situations that are structurally similar to yours and collect their outcome data.
- Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
- Distinguish cognitive optimism bias from strategic misrepresentation
Recognize that some forecast inflation is genuine bias and some is deliberate spin — they require different fixes.
- Update your forecast incrementally as new evidence arrives
Treat your forecast as a probability that should shift with each new piece of evidence, not a commitment that survives contradiction.
- Conduct post-project debriefs to contribute to the class data
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.