Coaching practices for Past Predictions Accuracy
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Past Predictions Accuracy, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I’m sure about things constantly and I have no idea if that confidence is earned
- 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 have all these confident hunches about how things will turn out, but I never commit to a specific call in advance
- However things turn out, I can always tell myself I was basically right because my predictions are vague enough to survive anything
- I’m guessing how this goal of mine will play out purely from my own picture of it, and it feels sure to work
Practices that may help
- 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. - 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 - Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - Make bold predictions — then check them
State a specific, testable prediction about the future and record it before you see the outcome.
Falsification Thinking - Pre-commit to resolution criteria before making a prediction
Define exactly what counts as "I was right" before the outcome happens.
Calibration Training - Affect Forecasting: Why You Mispredicted How You’d Feel
Daniel Gilbert and colleagues showed that people reliably misjudge both the intensity and duration of their future emotional reactions — overestimating how bad bad events will feel and how good good events will feel. The mechanism is "impact bias": we focus on the event and ignore the adaptive processes that will moderate it. This research is well-replicated and has direct implications for how you make decisions, build habits, and manage expectations. - 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 - 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 - Maintain a scored prediction log
Record predictions with explicit probabilities and score them when they resolve.
Calibration Training
Related concerns
- Prediction Tracking
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Treat forecasting accuracy as a skill that improves with practice
- Bold Predictions Testing
State a specific, testable prediction about the future and record it before you see the outcome.
Make bold predictions — then check them
- How To Make Accurate Predictions
Take prediction errors as performance feedback, not as proof that forecasting is futile.
- Prediction Vs Result
Define exactly what counts as "I was right" before the outcome happens.
Pre-commit to resolution criteria before making a prediction
- 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.
- Superforecasting At Work
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.
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