Audit Sampling: Why Population Completeness Comes Before Sample Selection
A perfectly selected sample cannot compensate for a population that is incomplete, incorrectly defined, or unrelated to the audit objective.
Define the population
The auditor identifies the population that is relevant to the assertion and audit objective.
Evaluate completeness
The auditor considers whether all items that should be in the population are actually included.
Sampling consequence
If the population omits relevant items, the sample can be statistically sound yet still fail to address the intended assertion.
Exam strategy
Before thinking about sample size or selection method, ask whether the population itself is complete and appropriate.
The takeaway
Sampling reliability starts with a complete, properly defined population; a good sample cannot fix a bad population.
Related Summit resources
FAQ
What is the key distinction?
Sampling reliability starts with a complete, properly defined population; a good sample cannot fix a bad population.
What should I identify first?
A perfectly selected sample cannot compensate for a population that is incomplete, incorrectly defined, or unrelated to the audit objective.
Why is this tested?
Understand why an auditor must evaluate the population before relying on a sample drawn from it.