Update beliefs by degrees, not wholesale

Treat new information as evidence that shifts probabilities, not as proof that changes everything.

Why it works

When people update beliefs in response to new information, they tend to either ignore it (conservatism bias) or overcorrect to near-certainty (representativeness). Bayesian updating — the mathematically correct approach — requires multiplying the prior probability by the likelihood ratio of the new evidence. Even an informal version of this (how much more likely would this evidence be if my hypothesis is true, versus false?) produces more calibrated beliefs.

How to do it

1. State your current probability estimate before looking at new evidence.
2. Ask: "How much more (or less) likely is this evidence if my hypothesis is correct vs incorrect?"
3. Shift your probability in proportion to that ratio — not to 0% or 100% unless the evidence is truly definitive.

Evidence

Bayesian reasoning is the normative standard in probability theory. People systematically deviate from it in both directions (over- and under-updating) depending on whether the evidence is vivid or abstract. Training in Bayesian reasoning improves calibration. (observational)

Formal Bayesian computation is not practical for everyday decisions; the goal is the habit of proportional updating, not precise calculation.

Sources

• Kahneman (2011), Thinking, Fast and Slow — review of representativeness and Bayesian deviation research

Common mistake

Treating a single confirming data point as near-proof — which is the representativeness heuristic exactly: a story that matches your hypothesis feels like it proves the hypothesis.