Bayes' Theorem Visualizer
Bayes' theorem helps us update our beliefs based on new evidence. This interactive tool visualizes how prior probability, sensitivity, and specificity affect the posterior probability in a medical testing scenario.
Adjust the sliders below and see how the results change in real-time!
Adjust Parameters
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0.5 1
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- Prior Probability (Prevalence): The initial probability of having a disease before testing
- Sensitivity: The ability to correctly identify those with the disease (true positive rate)
- Specificity: The ability to correctly identify those without the disease (true negative rate)
- Posterior Probability: The updated probability of having the disease after a positive test
- True Positive: Correctly identified as having the disease
- False Positive: Incorrectly identified as having the disease (also called a "Type I error")
- True Negative: Correctly identified as not having the disease
- False Negative: Incorrectly identified as not having the disease (also called a "Type II error")
Try These Examples
| Disease Prevalence (Prior Probability) | Test Sensitivity (True Positive Rate) | Test Specificity (True Negative Rate) |
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