 In this presentation, we will discuss the topic of develop, audit, explore. First, a word from our sponsor. Well, actually these are just items that we picked from the YouTube Shopping Affiliate Program, but that's actually good for you because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased, and used ourselves. Acer 27 inch monitor. I've been using an Acer monitor as my primary monitor for a few years now. This is the first Acer monitor that I have used after having used a series of different brands of monitors in the past. The Acer monitor has been performing well and I'm trusting the Acer brand more and more as I use the monitor. I have a 27 inch monitor which I think is ideal for what I do, which is of course the screen recording and the editing. If you would like a commercial free experience, consider subscribing to our website at accountinginstruction.com or accountinginstruction.thinkific.com where we have many different courses. You can purchase one at a time or have a subscription model given you access to all the courses. Courses which are well organized have other resources like Excel files and PDF files to download and no commercials. Spectations. When using analytical procedures, auditors need to have expectations to use as a type of benchmark. So it's going to be similar to a scientific type of method. We're going to say these are the expectations. This is what we expect to have. Then we're going to run the tests and see if they are close to what the expectations were. Now then the question would be, well, how do we come up with these expectations? The sources of the expectations could come from the financial data, from budgets, from things like industry standards. We might take the industry standard as benchmarks. Competitor information might help us to serve as a benchmark and expectations and management analysis is the types of things we may use for the benchmarks so that then we can run those analytical procedures, those kind of ratio type procedures, those types of things I would consider often done. We can imagine them being done within the audit office. We're going to run these types of analytical procedures to see if they make sense, make sense in accordance with our expectations, our expectations set in accordance with some type of benchmark. Next we want to discuss the concept of precision with relation to the quality of an expectation. So precision relates to the potential effectiveness of an analytical procedure, degree of reliance that can be put on the procedure and how closely does the expectation approximate the correct but not known amount. Amount of desired precision can differ with these factors including the purpose of the analytical procedure. Why are we putting the analytical procedure together? Function of materiality and required detection risk. So when we think about the detection risk, we're thinking about what the level of detection risk is. If the assertion being tested requires a low level of detection risk, the expectation needs to be very precise. In other words, when we consider our audit formula to consider what the detection risk is for this particular procedure, then if we're setting the detection risk to be low, that means that we want a low level of risk that we won't be detecting the problem and therefore we need more precision so that we would be more likely to catch the problem. So note it can be a little confusing when we think about these risk factors such as detection risk. If we're saying that we want a low detection risk, that means that we want a very precise type of test to make the detection risk the risk that we don't detect something low. Factors affecting the precision of analytical procedures, one desegregation which is going to be one of the major types of internal controls. If we have proper desegregation between duties, we're more likely to have better controls and therefore more reliable data plausibility and predictability of the relationship being studied. So when we think about the relationship, we're thinking about ratio analysis. Oftentimes we're comparing numbers. When we're thinking about analytical procedures, those things we can kind of do in the office and the auditing office as we compare the numbers out and therefore when we're comparing relationships of numbers, there could be different degrees of what the plausible relationship will be. So we have to know, we have to have an understanding as we go through these types of comparisons. What is the plausibility and predictability of the relationship that is being studied with the analytical procedures? The reliability of the data. So the data that we are using, of course, when we do analytical procedures in the office, it's only as good as the data that we are getting. So is the data that we have reliable or how reliable is it? And then we can go from there with our analytical procedures with what we have. Types of analytical procedures used to make the expectation. So what type of analytical procedures are we using in order to get to that expectation? These are things that are going to affect factors affecting the precision of analytical procedures. Tolerable, different size depends on. So what's going to be the tolerable difference? You can see this procedure kind of like a scientific type method. We have what would be similar to our hypothesis. This is going to be basically our benchmark, our expectation. And then we're going to perform the procedures and see how close they are to the expectation. Then of course the question is, well, what's going to be the tolerable amount of difference? There's going to be a difference because it's just an estimate, which we are now reperforming. What then would be a tolerable difference size? And that will depend on the significance of the account. So what type of account are we looking at? If it's a very important account, then possibly we're going to be more precise. If it's cash and cash is very important, then we probably want a low difference when we do these type of analysis. So that's one of the major things that's going to be effective. Degree and also of course always we're relating to materiality, what's going to be a material type of account, one that might affect decision makers as well. Degree of reliance that will put on substantive analytical procedures. So when we think about the audit as a whole, recall that analytical procedures is going to be one of the things that we do. The question is how reliant are we on the analytical procedures? If there's going to be other procedures that are going to basically cover this information, then possibly we don't need the difference could be larger in this analytical procedure and we can move on and basically do more testing other places. However, if we're relying more heavily on the analytical procedure itself, then we're going to want to have a closer obviously, or a lower level of tolerable difference to be more reliant on that information. Precision of the expectation. So obviously some expectations we might have are going to be more precise than others. So some expectations, we can be pretty precise in terms of what we think the results should be. Others, we're going to have a wider range of expectations. So then that would vary depending on what type of thing we're looking at. We're going to have an idea of what type of variance could be there. If we're comparing to industry standards, then the average might be on the industry standard, but there could be a fairly wide range of difference if we're comparing to something like an industry standard as the benchmark. The next thing we'll do of course is investigate the differences greater than the tolerable difference. So we're doing the analytical procedures. We're setting the tolerable difference and the most kind of basic type of analytical procedure you could think of with this would be to compare one year to the prior year and see what the dollar difference would be, as well as the percentage change differences and possibly take those and set some type of benchmark amount and then think about those types of changes that are going to be over and above the tolerable differences that would be set. Again, there's a whole lot of different types of analytical procedures we can do, but that would basically be the procedure we're having, right? We're going to say this is the expectations. Everything should be within this range. We would think anything that's going to be outside of this range within our analytical procedure, we will then dig into do further testings on. So then differences identified by substantive analytical procedures indicate an increased likelihood of misstatement. So obviously if we're outside the range, we're outside the norm, that doesn't mean that there's a misstatement for sure. It just means that there's something that's unusual. It's not like the norm if we're comparing to industry standards or for comparing to last year, it's different than industry standards or it's different or it's a significant increase or decrease as compared to the last year. And therefore that could be an indication that there's an error or we can simply look into it and find exactly what that difference is due to. It might not be an error, but we might want to look into it and just basically see what it is. So we're going through the quantification, the follow up with quantification, collaboration and evaluation. Now we'll take a look at these individually. So we have the quantification that's going to be our follow up type of procedure, determine whether the explanation or error can be can explain the observed difference. So obviously once we have a difference outside the norm, we're going to see if we can explain it, if we can quantify what that difference would be due to some circumstance or error, corroboration, obtain sufficient audit evidence that links the explanation to the difference and substantiate that the information, the explanation is reliable. So once we've basically said, Hey, this is going to be the difference, we believe what this difference is, but then we want to go in and actually test that we want to cooperate that information. So we're going to obtain sufficient audit evidence that links the explanation, what we believe the difference is due to to the difference. So then we're going to basically corroborate. And then we have evaluation, evaluate results of substantive analytical procedures to decide whether the desired level of assurance has been achieved. So once we've gone through this process, we're going to say, Okay, how does it look now? Have we achieved the desired level of assurance at this point in time?