Use reference classes to ground personal estimates in base rates
Before estimating how your situation will unfold, find similar past situations and check what happened.
Why it works
People generate predictions primarily from inside view — their own model of the specific case — which is systematically optimistic because it ignores how often similar plans fail in general. Reference class forecasting corrects this by grounding the prior in outside view: the statistical distribution of outcomes for similar cases. The two views are then combined rather than the inside view dominating.
How to do it
- Define your situation type: "This is a home renovation project / a software product launch / a behavior change attempt."
- Find data on how that type of project typically goes: timeline, cost overruns, success rates.
- Use that base rate as your starting probability, then adjust for features that make your case better or worse than average.
- Do not let the inside view increase your probability more than the specific distinguishing features actually justify.
Evidence
Reference class forecasting was developed by Daniel Kahneman and Amos Tversky and later formalized by Bent Flyvbjerg in infrastructure project planning. Studies show it reliably reduces planning fallacy — especially cost and time overruns — compared to inside-view estimates. (observational)
Reference class forecasting works best when good historical base rate data exist; for novel situations, the reference class itself is uncertain.
Sources
- Flyvbjerg (2006), from Nobel Prize to project management: getting risks right, Project Management Journal
- Flyvbjerg, B. (2006). From Nobel Prize to project management: Getting risks right. Project Management Journal, 37(3), 5-15.
Common mistake
Choosing the reference class that makes your project look most distinctive (therefore most likely to be exceptional) rather than the class that most honestly resembles it.
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More practices for Bayesian Thinking: How to Update Beliefs Rationally
- State your prior probability before seeing the evidence
Before looking at any data, commit to a numerical estimate of how likely something is.
- Update beliefs incrementally, not all at once
New evidence should shift your probability somewhat — rarely from 5% to 95% in one step.
- Evaluate evidence by its likelihood ratio, not by how it makes you feel
Ask how much more likely this evidence would be if you’re right versus if you’re wrong.
- Practice calibration by tracking your confidence predictions
Your "80% confident" beliefs should come true about 80% of the time — check that they do.
- Actively seek evidence that would disconfirm your belief
Ask: "What would change my mind?" and then look for exactly that.
- Translate beliefs into bets to reveal your true confidence
Would you bet $100 on that belief at even odds? The answer often reveals the gap between claimed and actual confidence.
Related concepts
- Thinking, Fast and Slow, Made Usable
Two systems, the biases they create, and when to slow down
- Mental Models: Charlie Munger’s Latticework Approach
Building the multi-disciplinary toolkit that lets you see what single-discipline thinkers miss
- Confirmation Bias: Seeing What You Expect to See
The most pervasive cognitive bias and the practices that actually chip away at it
- Expected Value Thinking: Deciding Under Uncertainty
The math of rational choice under uncertainty, its real limits, and how to use it anyway