# Audit Sampling: An Application to Substantive Tests of Account Balances

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Chapter Nine. Audit Sampling: An Application to Substantive Tests of Account Balances. Substantive Tests of Details of Account Balances. The statistical concepts we discussed in the last chapter apply to this chapter as well. Three important determinants of sample size are: - PowerPoint PPT Presentation

### Transcript of Audit Sampling: An Application to Substantive Tests of Account Balances

Chapter 9Audit Sampling: An Application to Substantive Tests of
Account Balances

Chapter Nine

Substantive Tests of Details of Account Balances

The statistical concepts we discussed in the last chapter apply to this chapter as well. Three important determinants of sample size are:

Desired confidence level.

Tolerable misstatement.

Expected misstatement.

Population plays a bigger role in some of the sampling techniques used for substantive testing.

Misstatements discovered in the audit sample must be projected to the population, and there must be an allowance for sampling risk.

McGraw-Hill/Irwin

Substantive Tests of Details of Account Balances

Consider the following information about the inventory account balance of an audit client:

The ratio of misstatement in the sample is 2%

(€2,000 ÷ €100,000)

Applying the ratio to the entire population produces a best

estimate of misstatement of inventory of €60,000.

(€3,000,000 × 2%)

3,000,000

100,000

98,000

2,000

Substantive Tests of Details of Account Balances

The results of our audit test depend upon the tolerable misstatement associated with the inventory account. If the tolerable misstatement is €50,000, we cannot conclude that the account is fairly stated because our best estimate of the projected misstatement is greater than the tolerable misstatement.

McGraw-Hill/Irwin

Monetary-Unit Sampling (MUS)

*

Monetary-Unit Sampling (MUS)

*

Monetary-Unit Sampling (MUS)

Advantages of MUS

When the auditor expects no misstatement, MUS usually results in a smaller sample size than classical variables sampling.

The calculation of the sample size and evaluation of the sample results are not based on the variation between items in the population.

When applied using the probability-proportional-to-size procedure, MUS automatically results in a stratified sample.

*

Monetary-Unit Sampling (MUS)

Disadvantages of MUS

The selection of zero or negative balances generally requires special design consideration.

The general approach to MUS assumes that the audited amount of the sample item is not in error by more than 100%.

*

Planning

• Define the population.

• The tolerable misstatement.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

Sampling may be used for substantive testing to:

Test the reasonableness of assertions about a financial statement amount (i.e. is the amount fairly stated). This is the most common use of sampling for substantive testing.

Develop an estimate of some amount.

Table 1

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

3. Determine the sample size, using the following inputs:

• The desired confidence level or risk of incorrect acceptance.

• The tolerable misstatement.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

Assume a client’s book value of accounts receivable is €2,500,000, and the auditor determined a sample size of 93. The sampling interval will be €26,882 (€2,500,000 ÷ 93). The random number selected is €3,977 the auditor would select the following items for testing:

Cumulative

Sample

Account

Balance

Euros

Item

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

The misstatements detected in the sample must be projected to the population. Let’s look at the following example:

Book value

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

Basic Precision using the Table

If no misstatements are found in the sample, the best estimate of the population misstatement would be zero euros.

€26,882 × 3.0 = €80,646 upper misstatement limit

McGraw-Hill/Irwin

Misstatements Detected

In the sample of 93 items the following misstatements were found:

€3,284 ÷ €21,893 = 15%

Because the Axa balance of €32,549 is greater than the interval of €26,882, no sampling risk is added. Since all the euros in the large accounts are audited, there is no sampling risk associated with large accounts.

Customer

Compute the Upper Misstatement Limit

We compute the upper misstatement limit by calculating basic precision and ranking the detected misstatements based on the size of the tainting factor from the largest to the smallest.

(0.15 × €26,882 × 1.4 = €5,645)

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

We compare the tolerable misstatement to the upper misstatement limit. If the upper misstatement limit is less than or equal to the tolerable misstatement, we conclude that the balance is not materially misstated.

*

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

In our example the upper misstatement limit of €150,621 is greater than the tolerable misstatement of €125,000, so the auditor concludes that the accounts receivable balance is materially misstated.

When faced with this situation, the auditor may:

Increase the sample size.

Perform other substantive procedures.

Request the client adjust the accounts receivable balance.

*

Auditor's Decision Based

on Sample Evidence

Not Materially Misstated

Correct decision

Does not support the fairness of the account balance

Risk of incorrect rejection (Type I)

Correct Decision

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

MUS is not particularly effective at detecting understatements. An understated account is less likely to be selected than an overstated account.

The most likely error will be reduced by €2,688

(– 0.10 × €26,882)

Tests of Account Balances

The sampling unit for non-statistical sampling is normally a customer account, an individual transaction, or a line item on a transaction. When using non-statistical sampling, the following items must be considered:

Identifying individually significant items.

Determining the sample size.

© The McGraw-Hill Companies 2010

Identifying Individually Significant Items

*

Sample

Size

× Confidence factor

Auditing standards require that the sample items be selected in such a way that the sample can be expected to represent the population.

McGraw-Hill/Irwin

© The McGraw-Hill Companies 2010

Calculating the Sample Results

One way of projecting the sampling results to the population is to apply the misstatement ratio in the sample to the population. This approach is known as ratio projection.

Assume the auditor finds €1,500 in misstatements in a sample of €15,000. The misstatement ratio is 10%.

If the population total is €200,000, the projected misstatement would be €20,000 (€200,000 × 10%)

McGraw-Hill/Irwin

© The McGraw-Hill Companies 2010

Calculating the Sample Results

A second method is the difference projection. This method projects the average misstatement of each item in the sample to all items in the population.

The projected misstatement would be €30,000 (€3 × 10,000).

Assume misstatements in a sample of 100 items total €300 (for an average misstatement of €3), and the population contains 10,000 items.

McGraw-Hill/Irwin

Example

*

The auditor decides . . .

Based on the results of the tests of controls, the risk of material misstatement is assessed as low.

The tolerable misstatement is €55,000, and the expected misstatement is €15,000.

The desired level of confidence is moderate based on the other audit evidence already gathered.

All customer account balances greater than €25,000 are to be audited.

McGraw-Hill/Irwin

Example

The auditor sent positive confirmations to each of the 110 (95 + 15) accounts selected. Either the confirmations were returned or alternative procedures were successfully used. Four customers indicated that their accounts were overstated and the auditors determined that the misstatements were the result of unintentional error by client personnel. Here are the results of the audit testing:

Amount of

Book Value

Audit Value

€2,000 ÷ 425,000 × €850,500

As a result of the audit procedures, the following projected misstatement was prepared:

*

© The McGraw-Hill Companies 2010

Why Did Statistical Sampling

Fall Out Of Favour?

Firms found that some auditors were over relying on statistical sampling techniques to the exclusion of good judgement.

*

Classical Variables Sampling

Classical variables sampling uses normal distribution theory to evaluate the characteristics of a population based on sample data. Auditors most commonly use classical variables sampling to estimate the size of misstatement.

*

Classical Variables Sampling

A sampling distribution is useful because it allows us to estimate the probability of observing any single sample result.

In classical variables sampling, the sample mean is the best estimate of the population mean.

McGraw-Hill/Irwin

Advantages

When the auditor expects a relatively large number of differences between book and audited values, this method will normally result in smaller sample size than MUS.

The techniques are effective for both overstatements and understatements.

*

Disadvantages

Does not work well when little or no misstatement is expected in the population.

To determine sample size, the auditor must estimate the standard deviation of the audit differences.

*

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

Defining the Sampling Unit

*

Tolerable misstatement – Estimated misstatement

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

The Confidence Coefficient (CC) is associated with the desired level of confidence. The desired level of confidence is the complement of the risk that the auditor will mistakenly accept a population as fairly stated when the true population misstatement is greater than tolerable misstatement.

Table 1

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

The year-end balance for accounts receivable contains 5,500 accounts with a book value of €5,500,000. The tolerable misstatement for accounts receivable is set at €50,000. The expected misstatement has been judged to be €20,000. The desired confidence is 95%. Based on work completed last year, the auditor estimates the standard deviation at €31.

Let’s calculate sample size.

5,500 × 1.96 × €31

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

Calculating the Sample Results

The sample selection usually relies on random-selection techniques. Upon completion, 30 of the customer accounts selected contained misstatements that totalled €330.20. Our first calculation is the mean misstatement in an individual account which is calculated as follows:

€330.20

125

Mean

misstatement

€14,575 = 5,500 × €2.65

SD =

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

If both limits are within the bounds of tolerable misstatement, the evidence supports the conclusion that the account is not materially misstated.

(€50,000)

€50,000

Lower

limit

(€1,653)

Projected

misstatement

€14,575

Upper

limit

€30,803

€0

Planning

• Define the population.

• The tolerable misstatement.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Planning

• Define the population.

Planning

• Define the population.

Planning

• Define the population.

Planning

• Define the population.

3. Determine the sample size, using the following inputs:

• The desired confidence level or risk of incorrect acceptance.

• The tolerable misstatement.

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement

7. Draw final conclusions.

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Supports the fairness of

Desired Level of

Confidence CC Value

Chapter Nine

Substantive Tests of Details of Account Balances

The statistical concepts we discussed in the last chapter apply to this chapter as well. Three important determinants of sample size are:

Desired confidence level.

Tolerable misstatement.

Expected misstatement.

Population plays a bigger role in some of the sampling techniques used for substantive testing.

Misstatements discovered in the audit sample must be projected to the population, and there must be an allowance for sampling risk.

McGraw-Hill/Irwin

Substantive Tests of Details of Account Balances

Consider the following information about the inventory account balance of an audit client:

The ratio of misstatement in the sample is 2%

(€2,000 ÷ €100,000)

Applying the ratio to the entire population produces a best

estimate of misstatement of inventory of €60,000.

(€3,000,000 × 2%)

3,000,000

100,000

98,000

2,000

Substantive Tests of Details of Account Balances

The results of our audit test depend upon the tolerable misstatement associated with the inventory account. If the tolerable misstatement is €50,000, we cannot conclude that the account is fairly stated because our best estimate of the projected misstatement is greater than the tolerable misstatement.

McGraw-Hill/Irwin

Monetary-Unit Sampling (MUS)

*

Monetary-Unit Sampling (MUS)

*

Monetary-Unit Sampling (MUS)

Advantages of MUS

When the auditor expects no misstatement, MUS usually results in a smaller sample size than classical variables sampling.

The calculation of the sample size and evaluation of the sample results are not based on the variation between items in the population.

When applied using the probability-proportional-to-size procedure, MUS automatically results in a stratified sample.

*

Monetary-Unit Sampling (MUS)

Disadvantages of MUS

The selection of zero or negative balances generally requires special design consideration.

The general approach to MUS assumes that the audited amount of the sample item is not in error by more than 100%.

*

Planning

• Define the population.

• The tolerable misstatement.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

Sampling may be used for substantive testing to:

Test the reasonableness of assertions about a financial statement amount (i.e. is the amount fairly stated). This is the most common use of sampling for substantive testing.

Develop an estimate of some amount.

Table 1

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Planning

• Define the population.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

3. Determine the sample size, using the following inputs:

• The desired confidence level or risk of incorrect acceptance.

• The tolerable misstatement.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

Assume a client’s book value of accounts receivable is €2,500,000, and the auditor determined a sample size of 93. The sampling interval will be €26,882 (€2,500,000 ÷ 93). The random number selected is €3,977 the auditor would select the following items for testing:

Cumulative

Sample

Account

Balance

Euros

Item

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

*

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

The misstatements detected in the sample must be projected to the population. Let’s look at the following example:

Book value

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

Basic Precision using the Table

If no misstatements are found in the sample, the best estimate of the population misstatement would be zero euros.

€26,882 × 3.0 = €80,646 upper misstatement limit

McGraw-Hill/Irwin

Misstatements Detected

In the sample of 93 items the following misstatements were found:

€3,284 ÷ €21,893 = 15%

Because the Axa balance of €32,549 is greater than the interval of €26,882, no sampling risk is added. Since all the euros in the large accounts are audited, there is no sampling risk associated with large accounts.

Customer

Compute the Upper Misstatement Limit

We compute the upper misstatement limit by calculating basic precision and ranking the detected misstatements based on the size of the tainting factor from the largest to the smallest.

(0.15 × €26,882 × 1.4 = €5,645)

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

We compare the tolerable misstatement to the upper misstatement limit. If the upper misstatement limit is less than or equal to the tolerable misstatement, we conclude that the balance is not materially misstated.

*

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Steps in MUS Sampling

In our example the upper misstatement limit of €150,621 is greater than the tolerable misstatement of €125,000, so the auditor concludes that the accounts receivable balance is materially misstated.

When faced with this situation, the auditor may:

Increase the sample size.

Perform other substantive procedures.

Request the client adjust the accounts receivable balance.

*

Auditor's Decision Based

on Sample Evidence

Not Materially Misstated

Correct decision

Does not support the fairness of the account balance

Risk of incorrect rejection (Type I)

Correct Decision

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

MUS is not particularly effective at detecting understatements. An understated account is less likely to be selected than an overstated account.

The most likely error will be reduced by €2,688

(– 0.10 × €26,882)

Tests of Account Balances

The sampling unit for non-statistical sampling is normally a customer account, an individual transaction, or a line item on a transaction. When using non-statistical sampling, the following items must be considered:

Identifying individually significant items.

Determining the sample size.

© The McGraw-Hill Companies 2010

Identifying Individually Significant Items

*

Sample

Size

× Confidence factor

Auditing standards require that the sample items be selected in such a way that the sample can be expected to represent the population.

McGraw-Hill/Irwin

© The McGraw-Hill Companies 2010

Calculating the Sample Results

One way of projecting the sampling results to the population is to apply the misstatement ratio in the sample to the population. This approach is known as ratio projection.

Assume the auditor finds €1,500 in misstatements in a sample of €15,000. The misstatement ratio is 10%.

If the population total is €200,000, the projected misstatement would be €20,000 (€200,000 × 10%)

McGraw-Hill/Irwin

© The McGraw-Hill Companies 2010

Calculating the Sample Results

A second method is the difference projection. This method projects the average misstatement of each item in the sample to all items in the population.

The projected misstatement would be €30,000 (€3 × 10,000).

Assume misstatements in a sample of 100 items total €300 (for an average misstatement of €3), and the population contains 10,000 items.

McGraw-Hill/Irwin

Example

*

The auditor decides . . .

Based on the results of the tests of controls, the risk of material misstatement is assessed as low.

The tolerable misstatement is €55,000, and the expected misstatement is €15,000.

The desired level of confidence is moderate based on the other audit evidence already gathered.

All customer account balances greater than €25,000 are to be audited.

McGraw-Hill/Irwin

Example

The auditor sent positive confirmations to each of the 110 (95 + 15) accounts selected. Either the confirmations were returned or alternative procedures were successfully used. Four customers indicated that their accounts were overstated and the auditors determined that the misstatements were the result of unintentional error by client personnel. Here are the results of the audit testing:

Amount of

Book Value

Audit Value

€2,000 ÷ 425,000 × €850,500

As a result of the audit procedures, the following projected misstatement was prepared:

*

© The McGraw-Hill Companies 2010

Why Did Statistical Sampling

Fall Out Of Favour?

Firms found that some auditors were over relying on statistical sampling techniques to the exclusion of good judgement.

*

Classical Variables Sampling

Classical variables sampling uses normal distribution theory to evaluate the characteristics of a population based on sample data. Auditors most commonly use classical variables sampling to estimate the size of misstatement.

*

Classical Variables Sampling

A sampling distribution is useful because it allows us to estimate the probability of observing any single sample result.

In classical variables sampling, the sample mean is the best estimate of the population mean.

McGraw-Hill/Irwin

Advantages

When the auditor expects a relatively large number of differences between book and audited values, this method will normally result in smaller sample size than MUS.

The techniques are effective for both overstatements and understatements.

*

Disadvantages

Does not work well when little or no misstatement is expected in the population.

To determine sample size, the auditor must estimate the standard deviation of the audit differences.

*

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

Defining the Sampling Unit

*

Tolerable misstatement – Estimated misstatement

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

The Confidence Coefficient (CC) is associated with the desired level of confidence. The desired level of confidence is the complement of the risk that the auditor will mistakenly accept a population as fairly stated when the true population misstatement is greater than tolerable misstatement.

Table 1

$ 775,000,000

40%

$ 310,000,000

800,000,000

40%

320,000,000

Cash

1,800

25,000,000

40%

10,000,000

$ 320,000,000

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

The year-end balance for accounts receivable contains 5,500 accounts with a book value of €5,500,000. The tolerable misstatement for accounts receivable is set at €50,000. The expected misstatement has been judged to be €20,000. The desired confidence is 95%. Based on work completed last year, the auditor estimates the standard deviation at €31.

Let’s calculate sample size.

5,500 × 1.96 × €31

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

Calculating the Sample Results

The sample selection usually relies on random-selection techniques. Upon completion, 30 of the customer accounts selected contained misstatements that totalled €330.20. Our first calculation is the mean misstatement in an individual account which is calculated as follows:

€330.20

125

Mean

misstatement

€14,575 = 5,500 × €2.65

SD =

© The McGraw-Hill Companies 2010

Applying Classical Variables Sampling

If both limits are within the bounds of tolerable misstatement, the evidence supports the conclusion that the account is not materially misstated.

(€50,000)

€50,000

Lower

limit

(€1,653)

Projected

misstatement

€14,575

Upper

limit

€30,803

€0

Planning

• Define the population.

• The tolerable misstatement.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Planning

• Define the population.

Planning

• Define the population.

Planning

• Define the population.

Planning

• Define the population.

3. Determine the sample size, using the following inputs:

• The desired confidence level or risk of incorrect acceptance.

• The tolerable misstatement.

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement

7. Draw final conclusions.

Performance

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Evaluation

6. Calculate the projected misstatement and the upper limit on misstatement.

7. Draw final conclusions.

Supports the fairness of

Desired Level of

Confidence CC Value