1. Audit sampling requires that the auditor collect only a relatively small sub-sample of data, thereby initiating detection risk, i.e., the risk. . . that, based on the sample the auditor takes, the auditor will fail to detect a material misstatement in the financial statements. Data analytics seems to present a panacea to that notion of risk because, by auditing 100% of the sample, detection risk by definition is lower.
   1. It seems like data analytics is the perfect answer to the audit risk problem. So, comment on a variety of reasons that auditors might not be willing to rely on data analytics to drive audit risk down.
   2. Which do you think is costlier: sampling or data analytics? What are the different costs between sampling and data analytics?
   3. What role does cost-benefit play in the choice between employing statistical sampling versus data analytics?
2. Data analytics enables auditors to audit all transactions, rather than just a sample of transactions.
   1. Do you think that as the use of data analytics increases on audit engagements, the need for sampling will decrease?
   2. What role might the PCAOB or AICPA play in helping auditors determine when and how to incorporate data analytics into the audit?