Coaching practices for Prediction Resolution Criteria

Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Prediction Resolution Criteria, these are the strongest matches in the current practice library.

Does this sound like the set of challenges you might be facing?

• However things turn out, I can always tell myself I was basically right because my predictions are vague enough to survive anything
• When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
• Someone hands me a forecast that says 2.3% and that crisp little number makes me trust it way more than I should
• I make calls all the time but I never write them down or check them later, so my confidence is just a feeling that floats free
• I catch myself rating this as likely to work mostly because I want it so badly

Practices that may help

1. Pre-commit to resolution criteria before making a prediction
   Define exactly what counts as "I was right" before the outcome happens.
   Calibration Training
2. Treat forecasting accuracy as a skill that improves with practice
   Take prediction errors as performance feedback, not as proof that forecasting is futile.
   Superforecasting
3. Beware false precision in forecasts and models
   Treat any precise probability or quantitative forecast with explicit suspicion about whether the model fits the domain.
   The Ludic Fallacy: When You Mistake Real Life for a Game
4. Maintain a scored prediction log
   Record predictions with explicit probabilities and score them when they resolve.
   Calibration Training
5. 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
6. 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.
7. 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.
8. 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
9. Calibration practice
   Predict your performance before each retrieval test, then compare prediction to outcome.
   Self-Regulated Learning: Taking Control of How You Learn
10. 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

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