Explain base-rate effects with posterior precision and expected natural-frequency counts from prevalence, sensitivity and specificity.
How to use this tool
- Choose the event or model and review the assumptions below.
- Enter the labeled inputs. Probability inputs are percentages from 0 to 100, not decimal fractions.
- Select Calculate and read the labeled outputs.
- Change an input and calculate again; Reset restores the example inputs.
Calculation method
P(actual positive | flagged) = sensitivity·prevalence / [sensitivity·prevalence + (1−specificity)·(1−prevalence)].
Assumptions and limits
A hypothetical screening or classification population; rates are assumed applicable and constant. Expected counts can be fractional. This is an educational base-rate model, not an individual diagnosis or certainty about observed counts.
Worked example
At prevalence 1%, sensitivity 90%, specificity 95% and hypothetical population 10,000, expected counts are TP = 90, FN = 10, FP = 495 and TN = 9,405. The true-positive share of all flags is 90/585, approximately 15.3846%. The false-positive rate among actual negatives is 5%, a different denominator from the false share of flags. These expected counts are a model illustration, not observed data.
Worked reading and practical questions
- Rare-Event Flags: Explain the Base Rate with Natural Frequencies
- Why Precision Changes When Prevalence Changes
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Frequently asked questions
What does this calculator assume?
A hypothetical screening or classification population; rates are assumed applicable and constant. Expected counts can be fractional. This is an educational base-rate model, not an individual diagnosis or certainty about observed counts.
How do I enter and update the inputs?
Use the labeled fields, then select Calculate. Percent fields use 0 to 100. After editing, calculate again; Reset restores the example inputs.