Sampling Statistical Nonstatistical Selection Classifier
Classify selection bias risk from the chosen method, population completeness, reproducibility, and whether statistical inference is required.
Description
Sampling inference needs a defensible selection process and population.
Sampling inference needs a defensible selection process and population. This screen distinguishes method labels from supplied process evidence.1
The returned values include Selection-bias risk, Suitable for requested inference. The How section below explains the calculation, input rules and interpretation.
When to use Sampling Statistical Nonstatistical Selection Classifier
Use Sampling Statistical Nonstatistical Selection Classifier when you need to evaluate the supplied records under the calculation and evidence rules below. The suitability output can be true with high risk when inference is not requested. No selection is performed or real statistical validity guaranteed.
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 Sampling Statistical Nonstatistical Selection Classifier 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 populationComplete, selectionMethod, selectionReproducible, statisticalInferenceRequired using the field table and calculation rules, then run the tool.
- Read risk, suitableForStatisticalInference. 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 |
|---|---|---|---|
| populationComplete | Population complete | boolean | Required |
| selectionMethod | Selection method | string; one of "haphazard", "judgment", "random", "systematic" | Required |
| selectionReproducible | Selection reproducible | boolean | Required |
| statisticalInferenceRequired | Statistical inference required | boolean | Required |
Validate method haphazard/judgment/random/systematic and three booleans. Random/systematic count as probability methods. Risk is low for baseSuitability, elevated for other probability methods, high otherwise. Output suitability is baseSuitability OR not inferenceRequired.
baseSuitability=probabilityMethod AND completePopulation AND reproducible Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| risk | Selection-bias risk | string; one of "elevated", "high", "low" |
| suitableForStatisticalInference | Suitable for requested inference | boolean |
{
"input": {
"populationComplete": true,
"selectionMethod": "systematic",
"selectionReproducible": true,
"statisticalInferenceRequired": true
},
"output": {
"risk": "low",
"suitableForStatisticalInference": true
}
}Limitations and assumptions
The suitability output can be true with high risk when inference is not requested. No selection is performed or real statistical validity guaranteed.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Extremely large text inputs are rejected. 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 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. For a complementary task, use Variables Sampling Mean Difference Ratio Selector Lookup: Variables-sampling method selection depends on usable baseline values and population assumptions. This selector uses a limited boolean decision table.
References
Similar or alternative tools
- 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.
- Variables Sampling Mean Difference Ratio Selector Lookup
Variables-sampling method selection depends on usable baseline values and population assumptions. This selector uses a limited boolean decision table.