Approaches to sampling

The two main approaches that can be applied in sampling:

  1. Judgmental sampling:

  2. Statistical sampling

Judgmental sampling also known as non-statistical sampling

Involves using experience and knowledge of clients business and circumstances to select and test the sample without any mathematical or statistical tools. The auditor does not rely on probability theory and requires the use of judgment in making sampling decisions.

Advantages of judgmental sampling

  1. Its well understood and refined by experience.
  1. Opportunity to bring expertise and knowledge into play in selecting and testing sample units.
  2. No special statistics knowledge required.
  3. No time wasted on the mechanics of statistical tools. More time is spent on auditing the sample units and less on the mechanics of constructing the sample and computing the mathematical implications of the results obtained.


  1. Unscientific it does not form a strong basis for defense, i.e., it is difficult to justify why one selected some items and left out others.
  2. Wasteful and large samples are selected. This is because in an effort to reduce the sampling risk the auditor attempts to select as many items as possible as opposed to statistical sampling where the size of the sample is precisely determined using probability theory.
  3. Samples may not be representative of the population and the results cannot be extrapolated.
  4. Danger of personal bias in sample selection.

Statistical sampling

Statistical sampling involves:

  • Use of random selection of a sample;
  • Use of probability theory to determine the sample size, evaluate quantitatively the sample results and measure sampling risk.
  • Statistical sampling differs from non- statistical sampling in that the auditor uses probability theory to measure sampling risk and to evaluate the sample results.


  • It is scientific and defensible. The auditor can justify the items selected because these are selected randomly.
  • Elimination of personal bias. The sample selected is unbiased.
  • Efficient as small samples are picked. Probability theory is applied in determining the precise sample size required.
  • Uniformity in different auditing firms hence comparisons are made possible.


  • Difficult to extract samples especially if documents are not sequentially numbered.
  • The need to follow a predetermined statistical approach may stifle initiative and the need to apply judgment.
  • The results may be misunderstood if the audit staff are not properly trained in the use of the technique.
  • It may not be suitable for all applications. Probability theory works best for large populations and therefore cannot be applied for small populations.
  • It is expensive due to the need for staff training.