Block Stratified Selection Coverage Adequacy Checker

Compare actual and planned block-stratified allocation fractions, report each coverage deficit, and calculate aggregate coverage percent.

Description

Stratified coverage must be considered by block as well as in aggregate.

Stratified coverage must be considered by block as well as in aggregate.^[1]

This tool compares actual and planned allocation fractions and reports each shortfall without certifying the selection design.

When to use Block Stratified Selection Coverage Adequacy Checker

Use this tool to compare actual and planned allocation fractions and reports each shortfall without certifying the selection design.

How to use Block Stratified Selection Coverage Adequacy Checker and how it works

Establish the observations, units, reference context and assumptions described below.
Enter actualAllocation, plannedAllocation and run the tool.
Read coveragePercent, deficits, uncoveredBlockCount using the meanings and sign conventions below.
Correct any rejected inputs and rerun; independently review the result under the stated scope.

Input field	Meaning	Value type	Requirement
actualAllocation	Actual sampled fraction for each block.	array of number	Required; branch conditions below
plannedAllocation	Planned sampled fraction for each block.	array of number	Required; branch conditions below

Supply aligned nonempty actual and planned allocation arrays, each entry a fraction from zero to one. Require planned total>0 and both totals<=1. Return 100*actualTotal/plannedTotal, each max(0,planned_i-actual_i) deficit and the number of positive deficits. Aggregate coverage can exceed 100 percent and over-allocation in one block does not remove another block's reported deficit.

coveragePercent=100*sum(actual)/sum(planned); deficit_i=max(0,planned_i-actual_i)

Use the stated ordering, units and branch conditions.

Output field	Meaning	Value type
coveragePercent	Actual total divided by planned total times 100; can exceed 100 despite block deficits.	number
deficits	Deficits, using the scope, ordering and rule explained above.	array of number
uncoveredBlockCount	Uncovered Block Count, using the scope, ordering and rule explained above.	number

json

{
  "input": {
    "actualAllocation": [
      0.4,
      0.3,
      0.2
    ],
    "plannedAllocation": [
      0.5,
      0.3,
      0.2
    ]
  },
  "output": {
    "coveragePercent": 89.99999999999999,
    "deficits": [
      0.09999999999999998,
      0,
      0
    ],
    "uncoveredBlockCount": 1
  }
}

Limitations and assumptions

The result summarizes supplied audit observations under the stated arithmetic or checklist rule. It does not inspect underlying documents, select sufficient audit evidence, make a professional assurance judgment or establish regulatory compliance. Empty or all-true input can describe only the recorded assertions.

Supply explicit values and dense aligned arrays. Unexpected fields, wrong types and nonfinite scalar values are rejected; fixed-token, alignment and other branch conditions are explained above. A successful false flag, empty match list or unknown-evidence list differs from an input error. Extremely large inputs or nonrepresentable results may fail.

Alternative or Complementary methods

Use Sampling Size Estimation Calculator for a related operation: Estimating a population mean requires a precision target, variability estimate and sampling model.

Use Attribute Sampling Deviation Rate Size Computation for a related operation: An observed attribute-sampling deviation rate is the fraction of examined items with recorded deviations.

References

Block Stratified Selection Coverage Adequacy Checker — tool.io

Similar or alternative tools

Sampling Size Estimation Calculator
Estimating a population mean requires a precision target, variability estimate and sampling model.
Attribute Sampling Deviation Rate Size Computation
An observed attribute-sampling deviation rate is the fraction of examined items with recorded deviations.