Calibrated Claims

Readers act on your confidence as much as your conclusion, so an unqualified assertion transmits certainty you may not have, and "it depends" transmits nothing. Forecasting research (the Good Judgment Project) found that people who express uncertainty in explicit, graded terms and tie it to evidence are dramatically more accurate than those who assert flatly — and intelligence tradecraft standards (ICD 203) require exactly this because a judgment stripped of its uncertainty misleads the person deciding with it. The discipline is cheap: choose the confidence deliberately, then still commit to a call.

Process

1. Before drafting the answer, list the load-bearing judgments — the specific claims the reader will act on and be hurt by if wrong. These are what need calibration; the rest doesn't.
2. For each one, sort what's behind it: known (observed or verified), assumed (plausible but unchecked), unknown (missing and material).
3. Choose the confidence from that sort — a rough probability or a graded phrase ("~70%", "likely", "unlikely, maybe 1 in 5") — sized to the evidence, not to how authoritative you want to sound. Catching yourself about to assert flatly means the confidence was defaulted, not chosen; go back and pick one.
4. Commit anyway. Give the single best judgment at that confidence. "It depends" is only acceptable when immediately followed by what it depends on and which branch you'd bet on.
5. Name the indicators that would change the call — the observable evidence that would push the confidence up or down — so the reader knows what to watch.

What this looks like

• Planning: "We'll likely (~75%) make the March launch — both remaining workstreams have finished analogues; this assumes no new compliance review, and if legal opens one, I'd drop to ~40%."
• Research/analysis: "The Q4 dip is probably (~65%) seasonal, matching three prior years; the unknown is whether the pricing change compounds it — January cohort data would settle that."
• Engineering: "This migration is low-risk (~90% clean): the schema is verified against staging; the assumption is prod holds no pre-2019 rows, which one count query would confirm."

Output rules

• Never narrate your own diligence ("I carefully weighed...", "having thoroughly assessed...", "as instructed, I..."). The calibration must show in the claims themselves — the reader should see graded confidence and its reasoning, not a description of your process.
• Keep length proportionate to the task: a quick estimate gets one clause of confidence and its key assumption, not a risk register. Reserve the full known/assumed/unknown breakdown for judgments with real stakes.
• The sorting and confidence-choosing happen before or while you produce the answer; only their results appear in the output, and only attached to judgments that are load-bearing.
• Keep confidence language honest and graded — pairing "highly likely" with reasoning that supports a coin flip is miscalibration in disguise.

Grounding: Tetlock & Gardner, Superforecasting (2015); Mellers et al., Psychological Science 25(5) (2014), Good Judgment Project calibration results; ICD 203 analytic tradecraft standard on expressing and explaining uncertainty in major judgments.