Sidak Correction Calculator
Sidak per test alpha1−(1−family alpha)^(1/m), independence assumption explicit; not Bonferroni or arbitrary correlated voxel control. Actual independently matched observations/units and protocol, not diagnosis, causal mechanism, clinical decision or validated individual prediction.
Description
Sidak correction allocates a family error level across a specified number of independent tests.
Sidak correction allocates a family error level across a specified number of independent tests.1
This tool calculates the per-test alpha with that independence assumption rather than deriving a voxel or cluster statistic threshold.
When to use Sidak Correction Calculator
This tool calculates the per-test alpha with that independence assumption rather than deriving a voxel or cluster statistic threshold.
How to use Sidak Correction Calculator and how it works
- Prepare family alpha, independent test count, using the input definitions and matching units below.
- Enter the required fields and run Sidak Correction 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 |
|---|---|---|---|
| callerFamilyAlpha | Caller Family Alpha. Use the units and matching context stated in the calculation below. | number | Required; fraction from 0 to 1; finite; other constraints below |
| actualIndependentTestCount | Actual Independent Test Count. Use the units and matching context stated in the calculation below. | number | Required; integer 1–1000000; finite; other constraints below |
Require family alpha from 0 inclusive to 1 exclusive and test count integer from 1 to 1000000. Evaluate -expm1(log1p(-alpha)/count) for numerical stability and return per-test alpha. Family alpha zero returns zero. The independent-test assumption must hold for the intended Sidak interpretation; the tool does not verify dependence.
perTestAlpha=1-(1-familyAlpha)^(1/testCount) 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": {
"callerFamilyAlpha": 0.05,
"actualIndependentTestCount": 2
},
"output": {
"result": {
"value": 0.02532056551910361
}
}
}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 Signal Detection Beta Calculator for a complementary operation: The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.
References
-
Sidak Correction 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.
- Signal Detection Beta Calculator
The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.