Signal Detection Criterion Calculator
Equal-variance Gaussian signal detection criterionc=−(zH+zFA)/2, actual fraction interior0–1; not dprimefromcounts, no extreme fraction correction silently supplied. Actual independently matched observations/units and protocol, not diagnosis, causal mechanism, clinical decision or validated individual prediction.
Description
In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.
In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.1
This tool calculates c from interior fractions without silently correcting rates of zero or one.
When to use Signal Detection Criterion Calculator
This tool calculates c from interior fractions without silently correcting rates of zero or one.
How to use Signal Detection Criterion Calculator and how it works
- Prepare hit fraction, false alarm fraction, using the input definitions and matching units below.
- Enter the required fields and run Signal Detection Criterion Calculator.
- Read result.value using the units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualHitFraction | Observed hit proportion strictly between zero and one; no endpoint correction is supplied. | number | Required; fraction from 0 to 1; finite; other constraints below |
| actualFalseAlarmFraction | Observed false-alarm proportion strictly between zero and one; no endpoint correction is supplied. | number | Required; fraction from 0 to 1; finite; other constraints below |
Require hit and false-alarm fractions strictly between 0 and 1. Use the helper's approximate standard-normal inverse quantile for each fraction and return minus half their sum as criterion c. No pseudocount or endpoint correction is supplied; equal-variance Gaussian interpretation is an assumption rather than a fitted finding.
c=-(normalQuantile(hit)+normalQuantile(falseAlarm))/2 Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric model result on the ratio, score, constant or density basis explained above. | number |
{
"input": {
"actualHitFraction": 0.75,
"actualFalseAlarmFraction": 0.25
},
"output": {
"result": {
"value": 0
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Signal Detection Beta Calculator for a complementary operation: The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.
Use Sidak Correction Calculator for a complementary operation: Sidak correction allocates a family error level across a specified number of independent tests.
References
-
Signal Detection Criterion Calculator — tool.io
Similar or alternative tools
- Signal Detection Beta Calculator
The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.
- Sidak Correction Calculator
Sidak correction allocates a family error level across a specified number of independent tests.