An auditor finds that the client's accounting system processes credit purchases of inventory at the payment date rather than the delivery date, potentially understating year-end accounts payable (completeness). The auditor decides to focus on testing of completeness starting with the population of payables transactions. Given the concern, what would be the most appropriate sampling method?

Question

BlackTom AI · Accepted Answer

The question asks which sampling method is most appropriate when focusing on testing completeness for payables transactions in a population where inventory purchases are recorded at payment date rather to delivery date.
Option 1: Haphazard. This is an informal, non-probabilistic approach that can introduce bias because there is no random selection mechanism, so it may not yield a representative sample of all payables transactions.
Option 2: Random. Random sampling gives each transaction in the population an equal chance of selection, which supports unbiased estimation of the population’s characteristics and is appropriate when the goal is to assess whether all liabilities have been captured (completeness). It minimizes systematic bias and allows for probabilistic inferences.
Option 3: Systematic. Systematic sampling selects items using a fixed interval (e.g., every 10th invoice). While efficient, it can miss patterns or clusters in the data, especially if timing around cutoffs (like year-end) affects recording, potentially introducing bias if the interval aligns with an underlying pattern.
Option 4: Block. Block sampling selects consecutive items as a unit (a block). This can be efficient for certain analyses but tends to reduce randomness and, if the block happens to align with a sampling window or cycle, it may not represent the entire population well, risking biased estimates for completeness.
In this context, completeness testing benefits from a probabilistic, unbiased selection method to accurately infer whether all payables have been recorded. Random sampling provides the best protection against systematic bias and supports valid inferences about the population of payables transactions, whereas the other methods have identifiable risks of bias, pattern locking, or inefficiency in this scenario.

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