Coaching practices for Likelihood Ratio Evidence
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Likelihood Ratio Evidence, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I’m leaning hard on the evidence that lines up with what I want to be true and brushing off the rest
- One thing happened that fits what I suspected and now I’m treating it as settled
- I want to know whether the evidence actually changed my mind or just confirmed where I already landed
- The story hangs together so neatly
- Someone fits the picture of a type so perfectly that I just assume that’s what they are
Practices that may help
- 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.
Bayesian Thinking: How to Update Beliefs Rationally - Update beliefs by degrees, not wholesale
Treat new information as evidence that shifts probabilities, not as proof that changes everything.
Base-Rate Neglect: Why We Ignore the Odds - State your prior probability before seeing the evidence
Before looking at any data, commit to a numerical estimate of how likely something is.
Bayesian Thinking: How to Update Beliefs Rationally - Identify which features are actually diagnostic
Separate features that genuinely differentiate categories from ones that just complete the picture.
The Representativeness Heuristic — Judging by Resemblance - Look up base rates before forming a resemblance judgment
Before deciding "this looks like X," ask how common X actually is in the relevant population.
The Representativeness Heuristic — Judging by Resemblance - Update beliefs incrementally, not all at once
New evidence should shift your probability somewhat — rarely from 5% to 95% in one step.
Bayesian Thinking: How to Update Beliefs Rationally - Treat small samples with explicit skepticism
A short sequence can look representative without being statistically reliable — adjust confidence for sample size.
The Representativeness Heuristic — Judging by Resemblance - Distinguish vividness from frequency
A memorable story is not evidence that something is common.
The Availability Heuristic: Why Memorable Feels Probable - Interrogate whether similarity is doing the work
When assessing probability or quality, ask whether you’re really judging similarity to a prototype.
Attribute Substitution: When Your Brain Answers a Different Question - Strip the narrative and restate as a bare frequency claim
Translate the vivid scenario into a dry statistical question to see if it still feels probable.
The Conjunction Fallacy — When "More Details" Feels More Likely
Related concerns
- Bayesian Evidence Strength
Ask how much more likely this evidence would be if you’re right versus if you’re wrong.
Evaluate evidence by its likelihood ratio, not by how it makes you feel
- Probability Vs Resemblance
When assessing probability or quality, ask whether you’re really judging similarity to a prototype.
Interrogate whether similarity is doing the work
- Proportional Response Bias
Before deciding how much time, money, or effort to assign, anchor the amount to the scale of the problem.
Design responses in proportion to actual scale before the emotion sets them
- Avoid Representativeness
The representativeness heuristic is the mental shortcut of judging probability by how closely something resembles a prototype or stereotype. It is fast and often useful, but it reliably misfires when it overrides base rates, produces the conjunction fallacy, or treats random-looking sequences as unlikely.
- Bayes Theorem Practical
Bayesian thinking is the practice of holding beliefs as probabilities and updating them systematically when new evidence arrives — rather than treating beliefs as simply true or false. The mathematical framework is well established; the challenge is building the habits of explicit probability estimation and honest belief updating that make it practical.
- Bayesian Reasoning Explained
Bayesian thinking is the practice of holding beliefs as probabilities and updating them systematically when new evidence arrives — rather than treating beliefs as simply true or false. The mathematical framework is well established; the challenge is building the habits of explicit probability estimation and honest belief updating that make it practical.
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