AUD: Risk, Evidence, and Sampling: Sampling Risk - YouTube

Hey, gang, we’re now going to take a look at some of the items about sampling risk.

And what we’re going to look at is we’re going to look at some of the sampling

risks and possibilities. There are issues related to substantive testing

risk and control testing. Let’s take a look at this and move to the next item

here. Sampling risk, and you’ll notice sampling risk in substantive testing can

result in the risk of an incorrect acceptance. Your sample said everything

was okay, but it didn’t catch the errors. Or conversely, the risk of an incorrect

rejection. You happen to just by random luck catch all of the mistakes and it,

therefore suggest that there’s multiple more. There were no more, just those,

but unfortunately now you reject it. Let’s move on. There’s also testing of

control risk. That is when you did the test you set control risk too low, you

should have done more or you set it too high and therefore, you’re ineffective,

you’re inefficient in your work because you did more work than you needed to. So

we’re going to take a look at each of those. Let’s move on to the next one.

Right here you’ll notice the sample indicates that the population is okay or

the population is wrong. And then the recorded value of the population, is it

really okay or is it really not okay? Well notice it says it’s okay, and it is.

The sample says it’s okay. Great, we’re in the same boat. But in this case the

sample says it’s okay, but it actually was wrong. That’s known as the risk of an

incorrect acceptance. You’ll notice here where this, where the sample says it’s

wrong, but it’s really okay. We just happen to catch all the bad things in the

sample. That’s the risk of an incorrect rejection. If we say the sample says

it’s wrong and it was wrong, that’s a correct decision as well. So it gives

you an idea of how these all face out. Let’s go to our next slide. When you’re

sampling controls as opposed to substantive testing you’re going to

sample the control and it says the control is strong and it is. Great, but

how about the sample says the control is working, it’s strong, but it really isn’t

you just happen to collect and look at the items that were right, one of the

very few. That is the risk of assessing control risk too low, you put too much

reliance on the system because your sample said it was okay, but if you

really had dug in you would have found out it was wrong. Conversely, sample

indication that it was wrong, that the controls didn’t work, but it really was

working, you just happen to catch the one or two mistakes made all year. Well

that’s the risk of assessing control risk too high. The problem with that isn’t

that you’re going to have a bad audit, it’s that you’re going to do a lot more

work than you need to. As opposed to when it says the controls don’t work and

they really don’t work, then you’ve properly assessed the risk. Let’s go on

to the next area. Let’s take a look at an example. An auditor selects 3 out of

18 customers listed in an accounts receivable list. The client’s customer

lists are customers E, J, and M.  Let's touch and go on to the next area.

So you can see we have all these customers here

and we’re going to select E, J, and M. Let’s, let’s tap again and there they are

E, J, and M. Those are the ones we’re going to test. Let’s go on to our next

slide and see what happens. Confirmation responses are received. This one was

correct, this one was correct and this one was correct. Sounds like from the

test work this, this thing is good, we’re good with it, right? So let’s move on.

No errors. Based on this, we believe it’s fairly stated, no further testing needed.

Notice we did check those 3. Ironically those were 3 of the only good ones. All

of the others just by happenstance were wrong. Look at that all those are wrong.

But our sample said everything was right. In this case, we would have accepted

that the accounts receivable is correctly reported, this is known as the risk of an

incorrect acceptance. Let’s go on to the next slide. All right in example 2 we’re

going to have the auditor selects 3 out of 18 customers listed for accounts

receivable and they’re G, K, and Q. Moving on. So we’re going to select

them. Let’s tap on the next item and there they are. We’re going to select

those 3 items right here. So let’s go on to our next slide. Based solely on that,

the accounts receivable is not sairly fated, listen to that one, fairly sta-,

that was good, huh? Ugh, you’d think I don’t even know how to speak English

here. Don’t tell Pete about this because he’ll have a field day on me, so please,

shh our secret that I bungled that up. In this case, Customer G said it’s wrong,

Customer Q said it’s right, Customer K said it’s wrong. Two out of the three

are wrong. Based on this we’re looking at and we conclude that accounts

receivable was stated fairly. The actual misstatement is immaterial. Notice here

when we looked at everything, everything else was correct. No other mistakes

whatsoever. It just happened that the two wrong items were two of the items we

looked at. So in this case, this is the risk of an incorrect rejection. We

rejected the balance and yet it was actually correct. It was only off by an

immaterial amount. And as you can see we’ve covered all the details that you

need to know before you’re ready to try another exercise.