Sampling error remains even when every selected record is measured perfectly; nonsampling error can remain even when every population unit is included. A census has no sampling variation from selecting a subset, but it can still contain omissions, duplicates, wrong classifications, or recording mistakes.
Translate statistical quality into business controls
For an invoice extract, ask:
- Coverage: Does the frame include every eligible invoice once?
- Cutoff: Which issue, posting, settlement, and extraction dates control?
- Measurement: Do the recorded dates mean what the data dictionary says?
- Classification: Are cash sales, credit memos, intercompany items, and ordinary trade invoices separated correctly?
- Processing: Did joins, filters, transformations, or manual edits change row counts or values?
- Linkage: Did invoice and payment records match completely and uniquely?
Finance data needs analogous controls for security identifiers, corporate actions, stale prices, currencies, survivorship, and return calculations.
Precision cannot absorb bias
The standard error and margin of error quantify a specified sampling mechanism. They do not estimate these nonsampling failures. Increasing Juniper's sample size would narrow its simple sampling interval while leaving a wrong population definition or missing-invoice bias untouched. Report sampling precision and data-quality limitations separately.
Put the concept to work
Understand this concept
- Distinguish coverage, nonresponse, measurement, classification, cutoff, processing, duplicate, and linkage errors from sampling variability.
Analyze this concept
- Design source, cutoff, classification, completeness, uniqueness, validity, and reconciliation checks for an accounting or finance data extract.
Learning resources
Choose a lesson, try an application, or inspect the sources behind this concept.
Build on these ideas
- Nonsampling error — Understand
To analyze this concept: Required. Control design responds to named nonsampling failure modes.
- Sampling frame — Analyze
To analyze this concept: Required. Frame controls address completeness, duplicates, eligibility, and cutoff.
- Sampling frame — Understand
To understand this concept: Required. Coverage error begins with a mismatch between the frame and target population.
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- Variable — Understand
To understand this concept: Helpful. Measurement error concerns how a defined characteristic was recorded.
Lessons
Worked examples and cases
- Audit Juniper's unit, population, frame, and sample
- Build and challenge Juniper's one-sample mean intervals
- Decide what Juniper's invoice samples actually support
Show 2 more examples and cases
Practice
- Compute Year 1 interval width and preserve its boundary
- Disposition a valid quartile-fence flag
- Identify what a larger random sample cannot repair
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Common mistaken ideas
- Mistaken idea: A narrow interval proves the data are unbiased
- Mistaken idea: An outlier must be deleted
- Mistaken idea: Random sampling eliminates data error
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Sources
Related concepts
Show 3 more related concepts
Use this idea next
- Nonsampling error — Analyze
Required level here: understand. Required. Control design responds to named nonsampling failure modes.
- Statistical inference — Analyze
Required level here: analyze. Required. Sampling precision cannot substitute for source and measurement controls.