Tolerable Deviation Rate Expected Deviation Inputs Lookup
Convert expected and tolerable attribute-sampling percentages into fractional expected item counts and report their percentage-point headroom.
Description
Expected and tolerable deviation rates describe different attribute-sampling assumptions.
Expected and tolerable deviation rates describe different attribute-sampling assumptions. Their implied counts can be fractional.1
The returned values include Expected deviations, Rate headroom, Tolerable deviation count. The How section below explains the calculation, input rules and interpretation.
When to use Tolerable Deviation Rate Expected Deviation Inputs Lookup
Use Tolerable Deviation Rate Expected Deviation Inputs Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. No sample size, upper confidence bound or whole-item rounding is supplied.
Prepare source-backed observations and the applicable engagement policy before entering facts. Use consistent monetary units and periods. Supply all boolean evidence flags explicitly, and treat a successful screen as a reason to continue professional review.
How to use Tolerable Deviation Rate Expected Deviation Inputs Lookup and understand its results
- Prepare source-backed observations and the applicable engagement policy before entering facts. Use consistent monetary units and periods. Supply all boolean evidence flags explicitly, and treat a successful screen as a reason to continue professional review.
- Enter expectedDeviationRatePercent, populationSize, tolerableDeviationRatePercent using the field table and calculation rules, then run the tool.
- Read expectedDeviations, rateHeadroomPercent, tolerableDeviationCount. 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 |
|---|---|---|---|
| expectedDeviationRatePercent | Expected deviation rate (percent) | number; minimum: 0; maximum: 100 | Required |
| populationSize | Population size | integer; minimum: 1 | Required |
| tolerableDeviationRatePercent | Tolerable deviation rate (percent) | number; minimum: 0; maximum: 100 | Required |
Require positive safe-integer population and finite rates 0–100. Return both fractional counts and signed percentage-point headroom; expected may exceed tolerable.
expectedCount=population×expectedRate/100; headroom=tolerableRate−expectedRate Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| expectedDeviations | Expected deviations | number; minimum: 0 |
| rateHeadroomPercent | Rate headroom (percent) | number |
| tolerableDeviationCount | Tolerable deviation count | number; minimum: 0 |
{
"input": {
"expectedDeviationRatePercent": 2,
"populationSize": 1000,
"tolerableDeviationRatePercent": 5
},
"output": {
"expectedDeviations": 20,
"rateHeadroomPercent": 3,
"tolerableDeviationCount": 50
}
}Limitations and assumptions
No sample size, upper confidence bound or whole-item rounding is supplied.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Sampling Statistical Nonstatistical Selection Classifier: Sampling inference needs a defensible selection process and population. This screen distinguishes method labels from supplied process evidence. For a complementary task, use Haphazard Selection Randomization Bias Warning Checker: Haphazard selection differs from supported probability selection. Warnings expose missing process evidence even when statistical inference is not requested.
References
Similar or alternative tools
- Sampling Statistical Nonstatistical Selection Classifier
Sampling inference needs a defensible selection process and population. This screen distinguishes method labels from supplied process evidence.
- Haphazard Selection Randomization Bias Warning Checker
Haphazard selection differs from supported probability selection. Warnings expose missing process evidence even when statistical inference is not requested.