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Math
Bayes' Theorem
P(A|B) from prior probabilities
Runs locally in your browser
Parameters
Results
- P(A|B)
- 16.1%
- P(B)
- 5.9%
- Change
- 1,510% vs P(A)
Classic example: rare event + test with false positives.
How it works
Update a prior probability to a posterior using Bayes' theorem.
Who it's for: Medical students, data scientists, and anyone interpreting conditional probability.
Computes P(A|B) from P(A), sensitivity P(B|A), and false positive rate P(B|¬A).
Shows how much the evidence shifts belief from the prior.
Classic use case: interpreting diagnostic test results.
How to use
- Enter P(A) — the prior probability of the condition.
- Enter P(B|A) — test sensitivity (true positive rate).
- Enter P(B|¬A) — false positive rate.
- Read P(A|B) as the updated posterior probability.
Good to know
- Low priors with imperfect tests often still yield surprisingly low posteriors — base rates matter.