Coaching practices for Prior Probability
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Prior Probability, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I want to know whether the evidence actually changed my mind or just confirmed where I already landed
- One compelling story or one impressive candidate completely swings my whole read, and afterward I can’t tell whether real new facts moved me or I just got swept up in how vivid and memorable that one case was.
- One thing happened that fits what I suspected and now I’m treating it as settled
- Someone fits the picture of a type so perfectly that I just assume that’s what they are
- I’m guessing how this goal of mine will play out purely from my own picture of it, and it feels sure to work
Practices that may help
- 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 - State your estimate before encountering vivid case information
Lock in a prior probability estimate before reading a compelling story or meeting a specific candidate.
Attribute Substitution: When Your Brain Answers a Different Question - 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 - 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 - Use reference classes to ground personal estimates in base rates
Before estimating how your situation will unfold, find similar past situations and check what happened.
Bayesian Thinking: How to Update Beliefs Rationally - Ask the base rate before evaluating the specific case
Before judging any individual instance, first establish how often this kind of thing happens in general.
Base-Rate Neglect: Why We Ignore the Odds - 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 - Correct for the recency amplification of availability
Recent events feel more probable than they are — apply an explicit recency discount.
The Availability Heuristic: Why Memorable Feels Probable - Identify the right reference class for your situation
Find a well-defined set of past situations that are structurally similar to yours and collect their outcome data.
Reference Class Forecasting - Run a premortem before committing
Imagine the project has already failed and work backwards to find why.
The Planning Fallacy — Why Your Estimates Are Always Wrong
Related concerns
- Prior Probability Bias
Before looking at any data, commit to a numerical estimate of how likely something is.
State your prior probability before seeing the evidence
- Base Rate First Reasoning
Base-rate neglect is the tendency to underweight or ignore prior probabilities (how often things happen in general) when vivid, specific information is available. Identified by Kahneman and Tversky, it is one of the most robustly replicated biases in judgment research, and it leads to systematic overconfidence in predictions about specific cases. Correcting it requires actively looking up or estimating base rates before evaluating individual information.
- Base Rate Question
Base-rate neglect is the tendency to underweight or ignore prior probabilities (how often things happen in general) when vivid, specific information is available. Identified by Kahneman and Tversky, it is one of the most robustly replicated biases in judgment research, and it leads to systematic overconfidence in predictions about specific cases. Correcting it requires actively looking up or estimating base rates before evaluating individual information.
- 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.
- Likelihood Ratio Evidence
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
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