Expected Cell Count Audit
Audit expected contingency-cell counts against a caller-defined asymptotic adequacy criterion.
Description
Expected contingency-cell counts affect the adequacy of asymptotic statistical approximations.
Expected contingency-cell counts affect the adequacy of asymptotic statistical approximations. A caller-defined minimum can identify cells needing further review.
Audit expected contingency-cell counts against a caller-defined asymptotic adequacy criterion.
When to use Expected Cell Count Audit
Use this for the following task: Audit expected contingency-cell counts against a caller-defined asymptotic adequacy criterion.
Prepare the observed records or explicit model parameters requested below. Keep probabilities as fractions and paired records aligned.
How to use Expected Cell Count Audit and understand its results
- Prepare the observed records or explicit model parameters requested below. Keep probabilities as fractions and paired records aligned.
- Enter expectedCounts, minimumExpectedCount using the field table, then run the tool.
- Read belowMinimumIndexes, smallCellCount, allCellsAdequate. Interpret flags, units, ordering and placeholder values using the rules below.
- Review any reported issue or failed check, correct the supplied values when appropriate, and verify the result in its intended context.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| expectedCounts | Expected counts | list of number; each entry: number | Required |
| minimumExpectedCount | Minimum expected count | number | Required |
Supply nonempty nonnegative expected counts and a positive minimum. Return zero-based below-minimum indices and their count.1
small cell if expectedCount < minimum; allAdequate = no small cells This summarizes the declared operation. Units, indexing, inclusivity and exceptional cases are explained in the surrounding text.
| Output field | Meaning | Value type |
|---|---|---|
| belowMinimumIndexes | Below minimum indexes | list of number |
| smallCellCount | Small cell count | number |
| allCellsAdequate | All cells adequate | boolean |
{
"input": {
"expectedCounts": [
10,
4,
20
],
"minimumExpectedCount": 5
},
"output": {
"belowMinimumIndexes": [
1
],
"smallCellCount": 1,
"allCellsAdequate": false
}
}Limitations and assumptions
Equality is adequate by this rule. This does not calculate expected counts, run a chi-square test or choose a replacement method.
The tool evaluates supplied observations or a declared model. It does not establish the biological cause of an observed pattern. Malformed requests produce input errors. A successfully evaluated request can still contain false flags, empty result lists or a result outside the supplied threshold; those outcomes have the meanings stated above.
Alternative or Complementary methods
Choose First Stage Instrument Strength for this operation: Compute the first-stage instrument-set F statistic from R-squared, sample size, and instrument count. For a complementary task, use Reciprocal Cross Outcome Shift for this operation: Compare reciprocal-cross phenotype distributions without asserting sex linkage from a difference alone.
The implementation reference documents the exact calculation, supported input shapes and returned fields.
References
-
Expected Cell Count Audit — tool.io
Similar or alternative tools
- First Stage Instrument Strength
Compute the first-stage instrument-set F statistic from R-squared, sample size, and instrument count.
- Reciprocal Cross Outcome Shift
Compare reciprocal-cross phenotype distributions without asserting sex linkage from a difference alone.