Haphazard Selection Randomization Bias Warning Checker

List selection-bias warnings and reject statistical suitability when the requested inference lacks supported probability selection.

Description

Haphazard selection differs from supported probability selection.

Haphazard selection differs from supported probability selection. Warnings expose missing process evidence even when statistical inference is not requested.^[1]

The returned values include Selection-bias warning present, Suitable for statistical inference, Warning count, Warnings. The How section below explains the calculation, input rules and interpretation.

When to use Haphazard Selection Randomization Bias Warning Checker

Use Haphazard Selection Randomization Bias Warning Checker when you need to evaluate the supplied records under the calculation and evidence rules below. No sample selection or real randomness is tested. Suitability can be true despite warnings when inference is not required.

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 Haphazard Selection Randomization Bias Warning Checker 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, randomnessEvidence, selectionMethod, selectionReproducible, statisticalInferenceRequired using the field table and calculation rules, then run the tool.
Read biasedSelectionRisk, suitableForStatisticalInference, warningCount, warnings. 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
randomnessEvidence	Randomness evidenced	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 and four booleans. Add haphazard/judgment warning, then incomplete population, nonreproducible selection and missing randomness. Return list/count, any-warning bias flag and joint suitability rule. Random/systematic are probability labels.

suitable=not inferenceRequired OR probabilityMethod AND complete AND reproducible AND randomness

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
biasedSelectionRisk	Selection-bias warning present	boolean
suitableForStatisticalInference	Suitable for statistical inference	boolean
warningCount	Warning count	integer; minimum: 0
warnings	Warnings	list of string

json

{
  "input": {
    "populationComplete": true,
    "randomnessEvidence": false,
    "selectionMethod": "haphazard",
    "selectionReproducible": true,
    "statisticalInferenceRequired": true
  },
  "output": {
    "biasedSelectionRisk": true,
    "suitableForStatisticalInference": false,
    "warningCount": 2,
    "warnings": [
      "haphazard_selection",
      "randomness_not_evidenced"
    ]
  }
}

Limitations and assumptions

No sample selection or real randomness is tested. Suitability can be true despite warnings when inference is not required.

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 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 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

Haphazard Selection Randomization Bias Warning Checker — tool.io

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.
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.