Coaching practices for Forecasting Accuracy
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Forecasting Accuracy, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I commit to one confident number
- When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
- Each project wraps up and I move straight on without ever writing down what I’d guessed versus what actually happened
- New information keeps arriving that should obviously move my estimate, but I just cling to the date I first committed to
- I catch myself shaving down the estimate I’m about to present
Practices that may help
- Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - 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 - 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. - Superforecasting
Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable. - 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 - 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 - 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 - Practice probability calibration
Regularly make probabilistic predictions and track your accuracy across many of them.
Hindsight Bias: Why Everything Seems Obvious in Retrospect - Practice probabilistic calibration by tracking your predictions
Assign explicit probability estimates to your predictions and track whether they come true at the right rate.
Base-Rate Neglect: Why We Ignore the Odds
Related concerns
- Forecasting Precision
Treat any precise probability or quantitative forecast with explicit suspicion about whether the model fits the domain.
Beware false precision in forecasts and models
- Forecast Accuracy Measurement
Keep a forecast log and score it — you cannot improve calibration without feedback on where you were over- or under-confident.
Score your own past predictions to calibrate your outside-view use
- Forecasting Accuracy Practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Treat forecasting accuracy as a skill that improves with practice
- 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 Make Better Forecasts
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
- How To Practice Forecasting
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
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