After 30+ journal entries, calculate your calibration…
Across 30+ decision journal entries, calculate your calibration by grouping decisions by stated confidence level (e.g., all 70% predictions) and checking whether that percentage actually occurred—use this ratio to adjust future confidence statements.
Why This Is a Rule
Calibration — the match between your stated confidence and actual accuracy — is the meta-skill of decision-making. A perfectly calibrated person's 70% predictions come true 70% of the time, their 90% predictions come true 90% of the time. Most people are systematically overconfident: their 90% predictions come true only 70% of the time. This overconfidence produces underestimation of risk, insufficient contingency planning, and surprised-Pikachu responses to predictable failures.
The calibration calculation requires accumulated data — individual decisions tell you about individual outcomes, but calibration is a statistical property of your prediction system as a whole. Thirty entries is the minimum for meaningful grouping: with fewer, each confidence bucket has too few entries for the percentage to stabilize.
The corrective is simple once the data exists: if your 90% predictions come true only 70% of the time, you know to treat your future "90% confident" feeling as 70% confidence. This recalibration transforms overconfident certainty into appropriately uncertain estimation — which, paradoxically, produces better decisions because you prepare for the 30% failure rate your feelings told you was only 10%.
When This Fires
- After accumulating 30+ decision journal entries (Decision journal entries need six fields captured before…) with confidence percentages
- During quarterly calibration reviews when you have enough data to group meaningfully
- When you suspect you're systematically overconfident or underconfident
- When building a personal calibration curve that improves over years of decision-tracking
Common Failure Mode
Skipping the confidence percentage during journaling (Decision journal entries need six fields captured before…) because "I don't know what number to put." Any number is better than none. The accuracy of the initial number doesn't matter — what matters is that the number exists so it can be calibrated over time. Your first 30 entries will have poorly calibrated percentages; your next 30 will be better because you've seen your calibration curve.
The Protocol
(1) After 30+ journal entries, group entries by stated confidence level. Typical buckets: 50-59%, 60-69%, 70-79%, 80-89%, 90-100%. (2) For each bucket, calculate: what percentage of predictions in this bucket actually came true? (3) Compare each bucket's actual rate to its stated confidence. Perfect calibration: 70% bucket → 70% actual. (4) Identify your calibration pattern: overconfident (actual < stated) is most common. Underconfident (actual > stated) is rarer but possible. (5) Apply the correction: if your 80% predictions come true 65% of the time, your personal "80% confident" maps to ~65% real probability. Adjust future decisions accordingly — build more contingency, gather more information, or reduce commitment size for the actual confidence level. (6) Re-calculate every 30 entries. Calibration improves with practice — watching your calibration curve tighten over time is itself motivating.