Coaching practices for Forecasting Accuracy Journal
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Forecasting Accuracy Journal, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I keep being so sure I’m right and then being wrong, and I have no way to see whether my confidence actually means anything
- Each project wraps up and I move straight on without ever writing down what I’d guessed versus what actually happened
- I make these probability guesses in my head
- I make calls about how things will go all the time, but I never write them down or check them after
- When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
Practices that may help
- Track your calibration across many predictions
Score your confidence levels against outcomes over dozens of predictions to find your systematic biases.
Decision Journaling: Learning to Decide Better Over Time - 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 - Keep a decision journal to score your EV estimates
Log your probability estimates and payoff predictions, then compare them to what happened.
Expected Value Thinking: Deciding Under Uncertainty - 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 - Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - Maintain a scored prediction log
Record predictions with explicit probabilities and score them when they resolve.
Calibration Training - 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 - 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. - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - 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.
Related concerns
- Brier Score Forecasting
Record predictions with explicit probabilities and score them when they resolve.
Maintain a scored prediction log
- Forecast Accuracy Log
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
- Forecast Accuracy Measurement
Keep a forecast log and score it — you cannot improve calibration without feedback on where you were over- or under-confident.
- Forecasting Accuracy
Represent your forecast as a range of likely outcomes, not a single predicted number.
Forecast a distribution, not a point estimate
- 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
- 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.
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