Coaching practices for How to Improve Predictions
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For How to Improve Predictions, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
- I make calls about how things will go all the time, but I never write them down or check them after
- 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 want to actually know whether my gut is any good — when I feel sure about something, am I right that often, or am I just confident?
- I’m sure about things constantly and I have no idea if that confidence is earned
Practices that may help
- Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - Calibration practice
Predict your performance before each retrieval test, then compare prediction to outcome.
Self-Regulated Learning: Taking Control of How You Learn - 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 - Maintain a scored prediction log
Record predictions with explicit probabilities and score them when they resolve.
Calibration Training - 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 - Track your estimates and calibrate
Compare your Fermi estimates to actual figures when you can, and use the gap to improve future estimates.
Fermi Estimation - 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. - 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. - 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
Related concerns
- Confidence Prediction Tracking
Assign explicit probability estimates to your predictions and track whether they come true at the right rate.
Practice probabilistic calibration by tracking your predictions
- 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
- Forecasting Calibration
Calibration training teaches you to match your stated confidence levels to your actual accuracy: if you say "70% confident," roughly 70% of those claims should turn out to be correct. Tetlock’s forecasting research and probability training studies show calibration is trainable with practice and feedback — the evidence here is specific and replicable.
- How To Make Accurate Predictions
Take prediction errors as performance feedback, not as proof that forecasting is futile.
- Prediction Accuracy Tracking
Assign explicit probability estimates to your predictions and track whether they come true at the right rate.
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