How does stratifying an account balance population for sampling differ from using data analytics to audit the entire$$ balance?

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Part $1$

A$.$

When stratifying an account balance for$$ sampling, the auditor is performing tests related to a sample of items divided by$$ stratums, however, the entire population is included in the stratums. In$$ contrast, using data analytics to audit the entire population means the auditor is no longer performing a sample. For either$$ method, the auditor would not need to be concerned about sampling risk since the entire population is included.

B$.$

When stratifying an account balance for$$ sampling, the auditor is still only performing tests related to a sample of items in each stratum of a population.$$ Thus, there is still a concern that the$$ auditor's sample may not be representative of each subpopulation in the account balance. In$$ contrast, using data analytics to audit the entire population means the auditor is no longer performing a sample. As a$$ result, the auditor would not need to be concerned about sampling risk.

C$.$

When stratifying an account balance for$$ sampling, the sampling unit is almost always the items making up the recorded dollar population. Auditors can use the items making up the recorded dollar population as the sampling unit for testing all audit objectives. $$Thus$,$ there is generally no concern that the$$ auditor's sample may not be representative of each subpopulation in the account balance. In$$ contrast, using data analytics to audit the entire population means the auditor is no longer performing a sample. As a$$ result, the auditor would not need to be concerned about sampling risk.

D$.$

In both stratifying an account balance population for sampling and using data analytics to audit the entire$$ balance, auditors risk making incorrect quantitative conclusions about the population.