Dontopedia

Stratified Sampling Example

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)

Stratified Sampling Example has 34 facts recorded in Dontopedia across 2 references, with 6 live disagreements.

34 facts·22 predicates·2 sources·6 in dispute

Mostly:has stratum(5), defines variable(3), has step(3)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (10)

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usesMethodUses Method(3)

containsContains(2)

alternativeToAlternative to(1)

appliesToApplies to(1)

comparesWithCompares With(1)

estimatedByEstimated by(1)

usedInUsed in(1)

Other facts (32)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

32 facts
PredicateValueRef
Has StratumEmails Stratum[1]
Has StratumReports Stratum[1]
Has StratumInvoices Stratum[1]
Has StratumMemos Stratum[1]
Has StratumOther Stratum[1]
Defines Variablestrata[1]
Defines Variablesample_sizes[1]
Defines Variableestimated_total_volume[1]
Has StepStep 1 Define Strata[2]
Has StepStep 2 Sample Each Stratum[2]
Has StepStep 3 Analyze[2]
Rdf:typeSampling Method[1]
Rdf:typeStatistical Method[2]
Uses Functionsum[1]
Uses Functionprint[1]
Calculation MethodVolume Estimation Formula[1]
Estimated Total Volume10000[1]
Implemented inPython[1]
Calculation Formulasample_sizes[stratum] * strata[stratum] / len(strata)[1]
Alternative toCluster Sampling Example[1]
Compares WithCluster Sampling Example[1]
Sampling Rate0.1[1]
Iteration Scopeall-strata[1]
Comparison Methodcluster-sampling-example[1]
Requires Complete Coveragetrue[1]
Sampling Strategyproportional[1]
Computes EstimateEstimated Total Volume[1]
Applied toArchive Scenario[2]
Has ImplementationPython Code Example[2]
PurposeEstimate Total Volume[2]
Instance ofStatistical Method[2]
Used forDocument Volume Estimation[2]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:SamplingMethod
labelbeam/1beb4978-4037-4cb3-b798-2b7033c17548
Stratified Sampling Example
hasStratumbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:emails-stratum
hasStratumbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:reports-stratum
hasStratumbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:invoices-stratum
hasStratumbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:memos-stratum
hasStratumbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:other-stratum
calculationMethodbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:volume-estimation-formula
estimatedTotalVolumebeam/1beb4978-4037-4cb3-b798-2b7033c17548
10000
implementedInbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:Python
definesVariablebeam/1beb4978-4037-4cb3-b798-2b7033c17548
strata
definesVariablebeam/1beb4978-4037-4cb3-b798-2b7033c17548
sample_sizes
definesVariablebeam/1beb4978-4037-4cb3-b798-2b7033c17548
estimated_total_volume
calculationFormulabeam/1beb4978-4037-4cb3-b798-2b7033c17548
sample_sizes[stratum] * strata[stratum] / len(strata)
usesFunctionbeam/1beb4978-4037-4cb3-b798-2b7033c17548
sum
usesFunctionbeam/1beb4978-4037-4cb3-b798-2b7033c17548
print
alternativeTobeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:cluster-sampling-example
comparesWithbeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:cluster-sampling-example
samplingRatebeam/1beb4978-4037-4cb3-b798-2b7033c17548
0.1
iterationScopebeam/1beb4978-4037-4cb3-b798-2b7033c17548
all-strata
comparisonMethodbeam/1beb4978-4037-4cb3-b798-2b7033c17548
cluster-sampling-example
requiresCompleteCoveragebeam/1beb4978-4037-4cb3-b798-2b7033c17548
true
samplingStrategybeam/1beb4978-4037-4cb3-b798-2b7033c17548
proportional
computesEstimatebeam/1beb4978-4037-4cb3-b798-2b7033c17548
ex:estimated-total-volume
typebeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:StatisticalMethod
labelbeam/4b662326-7816-4042-a1f3-5c788d13e31e
Stratified Sampling Example
hasStepbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:step-1-define-strata
hasStepbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:step-2-sample-each-stratum
hasStepbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:step-3-analyze
appliedTobeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:archive-scenario
hasImplementationbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:python-code-example
purposebeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:estimate-total-volume
instanceOfbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:statistical-method
usedForbeam/4b662326-7816-4042-a1f3-5c788d13e31e
ex:document-volume-estimation

References (2)

2 references
  1. ctx:claims/beam/1beb4978-4037-4cb3-b798-2b7033c17548
  2. ctx:claims/beam/4b662326-7816-4042-a1f3-5c788d13e31e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/4b662326-7816-4042-a1f3-5c788d13e31e
      Show excerpt
      [Turn 389] Assistant: Certainly! Let's consider a scenario where you need to estimate the volume of documents in a large organization's archive. The archive contains various types of documents, such as emails, reports, invoices, and memos,

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