Practice calibration by tracking your confidence predictions
Your "80% confident" beliefs should come true about 80% of the time — check that they do.
Why it works
Calibration is the match between stated confidence and actual accuracy. Most people are overconfident: they assign high probabilities to beliefs that are true far less often than claimed. Deliberate calibration practice — predicting, then tracking outcomes — has been shown to meaningfully improve the accuracy of confidence estimates over time, because it provides the feedback loop that intuition normally lacks.
How to do it
- Each week, write down five to ten predictions with explicit confidence levels (e.g., "75% confident the project ships on time").
- After the outcome is known, record whether you were right or wrong.
- Every month, compute your calibration for each confidence bucket: what fraction of your 70% predictions came true?
- Adjust your confidence levels based on the pattern — if your 80%s come true 60% of the time, you are overconfident.
Evidence
Calibration training is one of the few documented interventions that reliably improves the accuracy of probabilistic predictions. Work by Phil Tetlock and colleagues on superforecasters found that explicit calibration practice was among the distinguishing features of the most accurate forecasters. Lichtenstein and Fischhoff demonstrated the causal link directly — a brief training regimen with outcome feedback measurably improved calibration, establishing that the feedback loop, not innate skill, is what closes the confidence-accuracy gap. (observational)
Superforecaster research focuses on geopolitical and macroeconomic predictions; generalization to personal and professional decisions is plausible but less directly studied.
Sources
- Tetlock & Gardner (2015), Superforecasting, Crown Publishers
- Lichtenstein, S., & Fischhoff, B. (1980). Training for calibration. Organizational Behavior and Human Performance, 26(2), 149-171.
- Mellers, B., Ungar, L., Baron, J., Ramos, J., Gurcay, B., Fincher, K., Scott, S. E., Moore, D., Atanasov, P., Swift, S. A., Murray, T., Stone, E., & Tetlock, P. E. (2014). Psychological strategies for winning a geopolitical forecasting tournament. Psychological Science, 25(5), 1106-1115.
Common mistake
Using vague language ("probably," "likely") instead of numbers, making it impossible to ever check whether your calibration has improved.
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More practices for Bayesian Thinking: How to Update Beliefs Rationally
- State your prior probability before seeing the evidence
Before looking at any data, commit to a numerical estimate of how likely something is.
- Update beliefs incrementally, not all at once
New evidence should shift your probability somewhat — rarely from 5% to 95% in one step.
- Evaluate evidence by its likelihood ratio, not by how it makes you feel
Ask how much more likely this evidence would be if you’re right versus if you’re wrong.
- Actively seek evidence that would disconfirm your belief
Ask: "What would change my mind?" and then look for exactly that.
- 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.
- Translate beliefs into bets to reveal your true confidence
Would you bet $100 on that belief at even odds? The answer often reveals the gap between claimed and actual confidence.
Related concepts
- Thinking, Fast and Slow, Made Usable
Two systems, the biases they create, and when to slow down
- Mental Models: Charlie Munger’s Latticework Approach
Building the multi-disciplinary toolkit that lets you see what single-discipline thinkers miss
- Confirmation Bias: Seeing What You Expect to See
The most pervasive cognitive bias and the practices that actually chip away at it
- Expected Value Thinking: Deciding Under Uncertainty
The math of rational choice under uncertainty, its real limits, and how to use it anyway