A population is conceptual; a sampling frame is operational. The frame is the list, register, query, or selection mechanism that makes units available for sampling. A perfect frame contains each eligible population unit exactly once and nothing else. Real frames often fall short.
Juniper stipulates a deduplicated year-specific invoice list after the extraction cutoff. Before believing that stipulation in real work, an analyst would reconcile record counts and amounts to the subledger, test unique invoice keys, inspect late postings and status filters, and explain every exclusion.
Four ways a frame can miss
- Undercoverage: eligible units are absent, such as archived or late-posted invoices.
- Overcoverage: ineligible units remain, such as cash sales or credit memos.
- Duplication: one population unit appears more than once.
- Mismatch: the listed unit differs from the target unit, such as payment events standing in for invoices.
Random selection works only within the frame it receives. It does not pull missing invoices into the list or remove duplicates automatically.
Why accountants and finance analysts should care
An accounting sample drawn from a ledger filtered after year-end can miss cutoff errors. A return database that contains only surviving securities can miss failed firms. Both samples may be randomly selected and precisely summarized. The frame still points at the wrong population.
Put the concept to work
Understand this concept
- Distinguish the target population from the accessible frame and explain undercoverage, overcoverage, duplicate, and ineligible-unit risks.
Analyze this concept
- Reconcile a business sampling frame to source-system totals, eligibility rules, cutoff controls, unique keys, and excluded-record counts before selection.
Learning resources
Choose a lesson, try an application, or inspect the sources behind this concept.
Build on these ideas
- Observational unit — Analyze
To analyze this concept: Required. Unique-unit and duplicate checks depend on the intended row grain.
- Population — Understand
To understand this concept: Required. Frame coverage can be judged only against a defined target population.
- Sampling frame — Understand
To analyze this concept: Required. A frame reconciliation tests the specific ways the accessible list can differ from the target.
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Worked examples and cases
Practice
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Related concepts
Show 1 more related concepts
Use this idea next
- Nonsampling error — Analyze
Required level here: analyze. Required. Frame controls address completeness, duplicates, eligibility, and cutoff.
- Nonsampling error — Understand
Required level here: understand. Required. Coverage error begins with a mismatch between the frame and target population.
- Sample — Understand
Required level here: understand. Required. Actual selection occurs from the accessible frame.
Show 1 more next steps
- Sampling frame — Analyze
Required level here: understand. Required. A frame reconciliation tests the specific ways the accessible list can differ from the target.