Coaching practices for Honest Forecasting
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Honest Forecasting, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I catch myself shaving down the estimate I’m about to present
- Each project wraps up and I move straight on without ever writing down what I’d guessed versus what actually happened
- I commit to one confident number
- New information keeps arriving that should obviously move my estimate, but I just cling to the date I first committed to
- I build my estimate from my own optimistic story first and only glance at how long these things usually take at the very end as a sanity check
Practices that may help
- Distinguish cognitive optimism bias from strategic misrepresentation
Recognize that some forecast inflation is genuine bias and some is deliberate spin — they require different fixes.
Reference Class Forecasting - 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.
Reference Class Forecasting - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - 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.
Reference Class Forecasting - 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.
Reference Class Forecasting - Separate motivational optimism from your forecast
Let your ambition be honest about what it is — a desired outcome — without contaminating your probability estimate.
The Outside View - Reference Class Forecasting
Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific. - 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.
Bayesian Thinking: How to Update Beliefs Rationally - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting - Score your own past predictions to calibrate your outside-view use
Keep a forecast log and score it — you cannot improve calibration without feedback on where you were over- or under-confident.
The Outside View
Related concerns
- Goal Vs Forecast
Represent your forecast as a range of likely outcomes, not a single predicted number.
Forecast a distribution, not a point estimate
- How To Improve Forecasting
Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific.
- How To Practice Forecasting
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
Conduct post-project debriefs to contribute to the class data
- Decompose Forecasting Question
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Decompose complex questions into sub-questions
- How To Make Better Forecasts
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
- Range Based Forecasting
Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific.
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