Likelihood Ratio
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
A quantity comparing how strongly evidence supports one hypothesis against its alternative, computed as the probability of the evidence given the hypothesis divided by the probability of the evidence given its negation. Values above one support the hypothesis, values below one oppose it, and a value of one leaves belief unchanged. Its proposition is that evidence varies enormously in diagnostic power, so the ratio tells you how much a given observation should shift your confidence. Qualifier: the ratio updates belief only when combined with the prior.
SCAFFOLDING EFFECT
Reduce cognitive load
- Evidence weighting: Judge how strongly a specific observation should move your belief. - Hypothesis comparison: Compute the probability of the evidence under both the claim and its alternative. - Belief updating: Combine the ratio with your prior probability instead of reading it in isolation.
Anchor fast decisions
Evidence changes belief in proportion to how differently the two hypotheses predict it. An observation that both hypotheses predict equally carries no diagnostic weight regardless of how striking it appears. Comparing the two conditional probabilities isolates that weight, and applying it to a prior probability produces a revised belief that can be updated again as new evidence arrives.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Likelihood_functionverified
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