Sensitivity Calculator
Calculates sensitivity, the share of diseased cases detected by a test. This page keeps TP/(TP+FN) visible, calculates the worked values immediately, and explains how true positives and false negatives shape the reported sensitivity.
Supply the observations for sensitivity
Calculated sensitivity
Defining the statistical question for Sensitivity
For sensitivity, the page directly calculates sensitivity, the share of diseased cases detected by a test.
In this sensitivity calculation, the requested output is Sensitivity, not a general verdict about a population or decision. Interpret sensitivity with this condition in view: Its numerical meaning comes from TP/(TP+FN), and its substantive meaning comes from how the source quantities were measured.
When reporting sensitivity, analysts commonly use this calculation when describing diagnostic performance, event frequency, or risk comparison for explicitly defined numerators and denominators. Recalculate sensitivity from the same premise: The page therefore separates the input labels from the answer and leaves the defining relationship available for review.
Reading the source values for Sensitivity
To reconstruct sensitivity, the default condition is True positives = 80 cases; False negatives = 20 cases. These entries must describe one coherent dataset, study, model, or planning scenario; combining unrelated populations or periods can yield correct arithmetic for an invalid comparison; keep that fact with the sensitivity record.
- True positives: The worked entry is 80 cases; it sets one numerical component of sensitivity through TP/(TP+FN). For this sensitivity field, check the permitted domain before comparing software results; the interface accepts values at least 0 while following TP/(TP+FN).
- False negatives: The worked entry is 20 cases; it anchors one part of sensitivity through TP/(TP+FN). For this sensitivity field, keep its stated unit and group attached when copying the case; the interface accepts values at least 0 while following TP/(TP+FN).
Recalculate one intermediate term from TP/(TP+FN) and compare it with the displayed sensitivity magnitude; the result should remain consistent with the structure of TP/(TP+FN).
Understanding the next analysis step for Sensitivity
The same dataset may also support specificity when that quantity better matches the study question.
Interpreting the printed relationship for Sensitivity
TP/(TP+FN)
A practical sensitivity check begins with this point: Read the symbols as a map from the labeled inputs to sensitivity. Preserve parentheses, powers, roots, logarithms, denominators, tail rules, or ordering exactly as printed because changing any of them defines another statistic, a distinction that matters when relying on sensitivity.
Inspect the allowed domain of every entry before substituting numbers into TP/(TP+FN); record the outcome from TP/(TP+FN) before changing another input.
Checking the worked case for Sensitivity
A practical sensitivity check begins with this point: The displayed defaults are True positives = 80 cases; False negatives = 20 cases.
80 true positives and 20 false negatives give sensitivity .80.
One safeguard for sensitivity is straightforward: The live default result is Sensitivity 0.8. That fixed case is useful for checking a copied formula, spreadsheet, code revision, or unit convention without inventing a second dataset; use the same condition when comparing sensitivity values.
The evidence behind sensitivity should support this statement: A good manual reconstruction does not need to duplicate every interface step. Recalculate the most informative intermediate quantity in TP/(TP+FN), then confirm that its direction, sign, and approximate size agree with the displayed sensitivity; this context belongs beside any decision based on sensitivity.
Reconstructing the result in context for Sensitivity
An audit of sensitivity turns on a specific detail: Sensitivity is conditional on disease status and does not answer how many positive results are true.
Interpret sensitivity with this condition in view: A diagnostic or risk measure is conditional on the reference definition, denominator, population prevalence, and observation period.
Recalculate sensitivity from the same premise: Interpret sensitivity together with the sample construction, measurement scale, exclusions, and analysis date. Another decimal place cannot repair selection bias, incompatible definitions, an inappropriate distribution, or a reversed comparison; include that condition when boundary-testing sensitivity.
Applying an independent check for Sensitivity
Reconstruct the two-by-two table or source risks and confirm that cases, noncases, exposed, and comparison groups were not interchanged; keep that fact with the sensitivity record.
Read TP/(TP+FN) from left to right, preserving every denominator, transformation, and ordering rule; the result should remain consistent with the structure of TP/(TP+FN).
Vary true positives while holding the other entries fixed and predict the change before recalculating, a distinction that matters when relying on sensitivity. Then restore the example and vary false negatives; disagreement between the prediction and TP/(TP+FN) often reveals a transposed field, wrong scale, or mistaken direction; a second reading of sensitivity should consider the same point.
Auditing the method boundary for Sensitivity
The calculator evaluates the quantities supplied to TP/(TP+FN); it does not verify how observations were collected, whether assumptions were met, or whether sensitivity is the right endpoint for the decision at hand; use the same condition when comparing sensitivity values.
Boundary behavior deserves explicit attention; this context belongs beside any decision based on sensitivity. For sensitivity, check zero denominators, proportions outside their stated scale, impossible counts, insufficient observations, unsupported distribution parameters, and rounded inputs before treating the output as stable.
Write down units, groups, tails, and time boundaries beside the source values for sensitivity; record the outcome from TP/(TP+FN) before changing another input.
Documenting a reporting record for Sensitivity
Save the entered values (True positives = 80 cases; False negatives = 20 cases), the relationship TP/(TP+FN), the unrounded calculator output, and the date of analysis; make that point explicit in the source record for sensitivity. In this sensitivity calculation, also retain any exclusions, missing-data treatment, tail choice, confidence level, allocation rule, lag, or parameter convention that affects this particular method.
Report sensitivity with units or scale where applicable and with enough significant digits for the next calculation, which is the rule applied here for sensitivity. When reporting sensitivity, round the published value only after dependent arithmetic is complete, and label a revised input scenario as a new result rather than overwriting the original record.
Separate measured inputs from assumptions or tuning choices when rebuilding TP/(TP+FN); this helps separate a data issue from a method issue while auditing TP/(TP+FN).
Comparing scale, direction, and edge cases for Sensitivity
A magnitude check for sensitivity starts with the input scale; include that condition when boundary-testing sensitivity. To reconstruct sensitivity, counts, proportions, percentages, rates, standardized values, and transformed parameters are not interchangeable even when their bare numbers look similar.
Use TP/(TP+FN) to predict whether increasing true positives should raise, lower, or leave the answer unchanged; a clear statement of it makes sensitivity reproducible. A practical sensitivity check begins with this point: A sign reversal or implausible order of magnitude deserves investigation before any narrative interpretation is written.
Edge cases for sensitivity should be chosen from the method rather than at random: examine an allowable boundary, a central case, and a value near a denominator, tail, rank, or support limit when one exists; a second reading of sensitivity should consider the same point.
Testing the evidence needed for a decision for Sensitivity
Before using sensitivity in a decision, identify the action it is meant to inform and the consequence of error, keeping the sensitivity workflow transparent. The evidence behind sensitivity should support this statement: The calculator supplies a statistical quantity, while thresholds, costs, benefits, and acceptable uncertainty belong to the surrounding decision process.
For sensitivity, pair the displayed value with the evidence most capable of revealing its weaknesses: raw observations for a summary, counts for a rate, residuals for a fitted model, interval width for an estimate, or alternative assumptions for a design calculation.
In this sensitivity calculation, if true positives or false negatives comes from an estimate rather than a direct measurement, explain that additional uncertainty instead of presenting sensitivity as though every input were known exactly.
Practical questions about sensitivity
What exactly does sensitivity describe here?
When reporting sensitivity, it is the output of TP/(TP+FN) for the displayed true positives and false negatives; the entered condition does not by itself establish a broader population or causal claim.
How can the default sensitivity example be checked?
To reconstruct sensitivity, start from True positives = 80 cases; False negatives = 20 cases, reproduce one intermediate term in TP/(TP+FN), and compare with Sensitivity 0.8; restore the defaults before testing a second scenario so the records remain distinguishable.