Audit Sampling Square Root N Sample Size Calculator
Expose caller square-root audit heuristic plus minimum/population cap; no statistical confidence, representativeness or error-detection assurance.
Description
A square-root audit rule is a heuristic rather than a statistical confidence design.
A square-root audit rule is a heuristic rather than a statistical confidence design. Population and minimum caps change its final count.
Expose caller square-root audit heuristic plus minimum/population cap; no statistical confidence, representativeness or error-detection assurance.
When to use Audit Sampling Square Root N Sample Size Calculator
Use this tool to expose caller square-root audit heuristic plus minimum/population cap; no statistical confidence, representativeness or error-detection assurance.
Prepare the collection, exposure, scheduling or financial records required for this calculation. Keep row order and the declared units consistent.
How to use Audit Sampling Square Root N Sample Size Calculator and understand its results
- Prepare the collection, exposure, scheduling or financial records required for this calculation. Keep row order and the declared units consistent.
- Enter populationRecordCount, callerSqrtSamplingMultiplier, callerMinimumSampleCount using the field table and calculation rules, then run the tool.
- Read sqrtHeuristicSampleCount, boundedCallerSampleCount, samplingFraction. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| populationRecordCount | Population record count | integer | Required |
| callerSqrtSamplingMultiplier | Caller sqrt sampling multiplier | number | Required |
| callerMinimumSampleCount | Caller minimum sample count | integer | Required |
Use positive integer population, positive multiplier and nonnegative integer minimum. Return uncapped heuristic count, bounded count and sampling fraction.1
sample = min(population,max(ceil(multiplier × sqrt(population)),minimum)) Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| sqrtHeuristicSampleCount | Sqrt heuristic sample count | number |
| boundedCallerSampleCount | Bounded caller sample count | number |
| samplingFraction | Sampling fraction | number |
{
"input": {
"populationRecordCount": 100,
"callerSqrtSamplingMultiplier": 1,
"callerMinimumSampleCount": 20
},
"output": {
"sqrtHeuristicSampleCount": 10,
"boundedCallerSampleCount": 20,
"samplingFraction": 0.2
}
}Limitations and assumptions
No representativeness, confidence level or detection assurance is provided.
Numbers and computed results must be finite; overflow produces an error. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Location Audit Discrepancy Rate Percent Calculator: Preserve confirmed versus unresolved location audit discrepancy bounds with actual pooled record denominators; unlocated items are not automatically compliant or stolen. For a complementary task, use Catalogue Record Completeness Field Counter: Audit caller-required catalogue fields across aligned records, distinguishing complete records from pooled field coverage; actual schema/presence criteria are caller established.
References
Similar or alternative tools
- Location Audit Discrepancy Rate Percent Calculator
Preserve confirmed versus unresolved location audit discrepancy bounds with actual pooled record denominators; unlocated items are not automatically compliant or stolen.
- Catalogue Record Completeness Field Counter
Audit caller-required catalogue fields across aligned records, distinguishing complete records from pooled field coverage; actual schema/presence criteria are caller established.