Signal Detection Beta Calculator
Equal-variance Gaussian likelihood criterionβ=exp[(zFA²−zH²)/2], not dprimeorc; actual interior fractions. Actual independently matched observations/units and protocol, not diagnosis, causal mechanism, clinical decision or validated individual prediction.
Description
The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.
The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.1
This tool calculates beta from supplied interior hit and false-alarm fractions under the equal-variance model.
When to use Signal Detection Beta Calculator
This tool calculates beta from supplied interior hit and false-alarm fractions under the equal-variance model.
How to use Signal Detection Beta 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 Beta 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 approximate standard-normal inverse quantiles and return exp((zFalseAlarm^2-zHit^2)/2). Beta is dimensionless and positive; zero/one rates are errors and no endpoint correction or sensitivity estimate is provided.
beta=exp((zFalseAlarm^2-zHit^2)/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": 1
}
}
}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 Criterion Calculator for a complementary operation: In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.
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 Beta Calculator — tool.io
Similar or alternative tools
- Signal Detection Criterion Calculator
In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.
- Sidak Correction Calculator
Sidak correction allocates a family error level across a specified number of independent tests.