Final answer:
Sampling error, which includes chance error and bias, is the risk of reaching an incorrect conclusion in auditing due to a non-representative sample. Large sample sizes and random selection help mitigate this risk and improve the study's reliability.
Step-by-step explanation:
The risk that an auditor will reach an incorrect conclusion because a sample is not representative of the population is known as sampling error. This type of error can manifest as chance error or bias. Chance error can occur if the sample size is too small, resulting in a decreased likelihood that the sample will accurately reflect the population. To mitigate chance error, it's advisable to use a larger sample. Bias, on the other hand, occurs when the sample is not selected randomly, leading to certain members of the population having a lower probability of being chosen. An example of bias is conducting a survey during specific times that exclude a portion of the intended population, thus skewing the results. This can lead to incorrect conclusions about the population, and therefore, biased samples should be avoided to maintain reliability and accuracy in research.