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Researchers all over the world have begun to question the sampling techniques applied, underlying its disadvantages and risks.

John Wendell and Josef Schmee (Wndell and Schmee,1996, pp.825) noted that statistical sampling auditing methods can create problems that do not apply to a large sample of items. Populations are usually small and the number of errors accepted, even in the case of a large sample of elements, is almost zero, setting aside the central limit theorem. Central Limit Theorem (CLT) is a statistical theory, which argues that, given a sufficiently large sample from a population diverse enough, the average of all results will be approximately equal to the average population. In addition, evidence will follow approximately a normal distribution model (eg, bell-shaped curve), with variations that reflect the source population variance divided by sample size. As a rule, a sample of 50 elements is necessary for the CLT to be applied.