Final answer:
Projected misstatements, also known as sampling errors, are estimates of misstatements in a population based on a sample in an audit. Inferential statistics, including point estimates and confidence intervals, are used to make these estimates. To improve accuracy, sample size can be increased and the sample should be representative of the population.
Step-by-step explanation:
The misstatements you're referring to in an audit context that are the auditor's best estimate of misstatements in a population, extrapolated from misstatements identified in an audit sample, are known as projected misstatements or sampling errors. These are errors that occur when an auditor takes a representative sample and extends the findings to the whole population. Inferential statistics play a crucial role in this process by providing tools like point estimates and confidence intervals, which help in forming a reasonable estimate of the population parameter based on sample data.
In the insurance company example where the aim is to determine the proportion of medical doctors involved in one or more malpractice lawsuits, different aspects like the population (all medical doctors), sample (500 doctors chosen), parameter (actual proportion of doctors involved in lawsuits), statistic (proportion found in the sample), and data (information gathered about the lawsuits) can be identified in the study design.
To achieve a more accurate estimate and lower the sampling error, measures such as increasing the sample size or ensuring that the sample is more representative of the population can be undertaken. The margin of error, such as the ±3 percent in polling, indicates the range within which the true value in the population is expected to lie with a certain level of confidence.