Variables Sampling Mean Difference Ratio Selector Lookup
Recommend mean-difference, ratio, further judgment, or no support for variables sampling from supplied reliability, homogeneity, and difference expectations.
Description
Variables-sampling method selection depends on usable baseline values and population assumptions.
Variables-sampling method selection depends on usable baseline values and population assumptions. This selector uses a limited boolean decision table.1
The returned values include Sampling method, Selection supported. The How section below explains the calculation, input rules and interpretation.
When to use Variables Sampling Mean Difference Ratio Selector Lookup
Use Variables Sampling Mean Difference Ratio Selector Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. varianceHigh is validated but unused. No sample size, estimator, confidence level or actual statistical suitability is established.
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 Variables Sampling Mean Difference Ratio Selector 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 differenceExpected, hasReliableBookValue, hasReliableExpectedValue, populationHomogeneous, varianceHigh using the field table and calculation rules, then run the tool.
- Read method, supported. 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 |
|---|---|---|---|
| differenceExpected | Difference expected | boolean | Required |
| hasReliableBookValue | Reliable book value | boolean | Required |
| hasReliableExpectedValue | Reliable expected value | boolean | Required |
| populationHomogeneous | Population homogeneous | boolean | Required |
| varianceHigh | Variance high | boolean | Required |
Validate five booleans. If book or expected value unreliable, return not_supported/false. Otherwise homogeneous and differenceExpected gives mean_difference; neither gives ratio; mixed gives judgment_required. All supported branches return true.
unreliable baseline→not_supported; homogeneous+expectedDifference→mean_difference Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| method | Sampling method | string; one of "judgment_required", "mean_difference", "not_supported", "ratio" |
| supported | Selection supported | boolean |
{
"input": {
"differenceExpected": true,
"hasReliableBookValue": true,
"hasReliableExpectedValue": true,
"populationHomogeneous": true,
"varianceHigh": true
},
"output": {
"method": "mean_difference",
"supported": true
}
}Limitations and assumptions
varianceHigh is validated but unused. No sample size, estimator, confidence level or actual statistical suitability is established.
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. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
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 Ratio Projection Misstatement Calculator: Ratio projection scales a signed sample difference to the population. Positive differences describe recorded overstatement; negative differences describe understatement.
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.
- Ratio Projection Misstatement Calculator
Ratio projection scales a signed sample difference to the population. Positive differences describe recorded overstatement; negative differences describe understatement.