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Math

Bayes’ Theorem Probability Calculator

Enter the prior P(A) (base rate of event A), the sensitivity P(B|A) (probability the test is positive when A is true) and the false-positive rate P(B|¬A) (probability the test is positive when A is false). The tool instantly returns the posterior given a positive test P(A|B+), the posterior given a negative test P(A|B−), specificity, and the positive/negative likelihood ratios — the same maths behind interpreting medical tests, spam filters and any binary classifier. It also makes the "base-rate fallacy" obvious to play with.

Posterior given positive test P(A|B+)

7.76%

Probability A is true after seeing a positive result.

Posterior given negative test P(A|B−)

0.22%

Probability A is still true after seeing a negative result.

Belief update

0% 50% 100%

Prior 1.00% → posterior 7.76% after positive

Specificity (1 − FPR)

90.40%

Positive likelihood ratio LR+

8.33

LR+ = sensitivity / FPR; > 10 = strong evidence for A.

Negative likelihood ratio LR−

0.221

LR− = (1 − sensitivity) / specificity; < 0.1 = strong evidence against A.

Inputs are percentages (0–100). When the base rate is low (say 1%) even a 95%-accurate test produces more false positives than true ones — try dialling P(A) and FPR down to feel the base-rate fallacy.

Formula

P(A|B+) = P(B|A) · P(A) / [P(B|A) · P(A) + P(B|¬A) · (1 − P(A))] P(A|B−) = (1 − P(B|A)) · P(A) / [(1 − P(B|A)) · P(A) + (1 − P(B|¬A)) · (1 − P(A))]

Frequently asked

What's the difference between Bayes' theorem and conditional probability?

Conditional probability P(A|B) is the definition — “the probability of A given B has occurred”. Bayes' theorem is the rule you use to flip that conditional around: when you know P(B|A) (e.g. a test's sensitivity) but want P(A|B) (the chance the patient is sick given a positive result), you apply P(A|B) = P(B|A)·P(A) / P(B). In short, conditional probability is the concept; Bayes' theorem is the formula for converting between the two directions — and it always requires the base rate P(A).

Why doesn't a positive result from a 95%-accurate test mean a 95% chance of disease?

Because “accuracy” ignores the base rate. Say the disease prevalence is 1% (1 in 100), sensitivity 95% and specificity 95%. Out of 100 people: 1 sick, 99 healthy. The 1 sick person tests positive with 95% probability → ~0.95 true positives. The 99 healthy people test positive at the 5% FPR → ~4.95 false positives. So among everyone who tests positive, only 0.95 / (0.95 + 4.95) ≈ 16% are actually sick — nowhere near 95%. The lower the prevalence, the worse the false-positive problem. This is the base-rate fallacy. Boosting the posterior is not about pushing sensitivity higher — it's about higher specificity (lower FPR) or screening only high-risk groups (higher P(A)).

What happens if I enter 0% or 100%?

Edge values are mathematically fine. P(A) = 0 means the event cannot occur, so the posterior is always 0 regardless of the test; P(A) = 100% means it definitely occurs and the posterior is always 100%. Setting sensitivity = 100% and FPR = 0% is the “perfect test” — a positive result guarantees the disease, but LR+ = 1/0 is technically infinite (infinite information). The tool labels such degenerate cases as ∞ rather than erroring.

Can I chain it across multiple sequential tests?

Yes — chaining is the whole point. Feed today's posterior in as tomorrow's prior, then re-enter the test's sensitivity and FPR. E.g. prior 1% → first positive → posterior 7.8%; treat that 7.8% as the new prior, run the same test again and get a second positive → ~41.4%; a third positive pushes you to ~85.4%. This only works when the two test results are conditionally independent — typically by using tests based on different principles. Re-running the exact same kit on the same sample tends to give correlated errors, which is why real-world clinical guidelines usually require a confirmatory test of a different type (e.g. ELISA followed by Western blot for HIV).

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