 In this presentation, we will discuss types of audit sampling. Auditing standards recognize and permit both statistical and non-statistical methods of audit sampling. So what are statistical and non-statistical methods of audit sampling? We'll start off with the statistical method, use of the laws of probabilities to compute sample size and evaluate results. The auditor uses the most efficient sample size and quantifies sampling risk. So when we think about statistical sampling as opposed to non-statistical sampling, we're thinking about a more formal type of sample selection and the more formal sample selection using statistical mathematical analysis can give us a more formal approach and give us more exact types of calculations than a non-statistical method. Non-statistical sampling auditor does not use statistical techniques to determine sample size, select the sample items, or measure sampling risk. And of the two, you might be saying, well, why would we do one or the other? In other words, why wouldn't we use statistical sampling when it seems to be the more accurate way to go? Of course, part of that is going to be cost effectiveness and complexity with statistical sampling. The advantages of statistical sampling are it can design an efficient sample, so we can design an efficient sample. And therefore, when we think about the sample size, what we want to do is to create a population as small as possible that we can do the least work as we need. In other words, if you are thinking about pulling people to get their opinion or the population's opinion on something, you would like to pull as few people as possible in order to get an accurate opinion. And that's what statistical sampling is better at doing, is better at giving us a very efficient sample size. Whereas if we were just to basically not use statistical sampling, we would have to select a sample size that seems reasonable without being as efficient and therefore probably be overestimating the sample size, probably leaning towards that side of things, and therefore possibly doing more testing as a result of not being as accurate on the sample can measure the sufficiency of evidence obtained. Once we have the evidence, we can know we don't have to just say, hey, look, the evidence says this, we can have more accurate type of statistical measures as to how accurate the sample can be using statistical measures, as opposed to saying, well, this is just what our sample says with regards to this assertion. And we're just going to apply it out. We give more