Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
58 Cards in this Set
- Front
- Back
|
What is sampling? |
Selecting representative items from a population, examining those selected items and drawing a conclusion about the population based on the results derived from the examination of the selected items |
|
|
What is the main issue in sampling? |
Choosing a sample that is representative of the population |
|
|
Example of Discrete Variables |
Yes/no decision whether to authorize payments of invoices |
|
|
How are discrete variables tested |
Tested using attribute sampling |
|
|
Example of Continuous Variables |
Monetary amounts of A/R |
|
|
How are continuous variables tested |
Tested using variables sampling |
|
|
Most important distribution curve |
Normal distribution - aka the bell curve. Values form a symmetrical bell-shaped cure centered around the mean.
In a normal distribution, the mean, median, and mode are the same and the tails are identical |
|
|
Mean |
Arithmetic average of a set of numbers |
|
|
Median |
Middle value if data are arranged in numerical order - the 50th percentile |
|
|
Mode |
Most frequently occurring value. If all values are unique no mode exists |
|
|
Which way do accounting distributions tends to skew? |
Positively skewed - To the right, the right tail is longer. mean > mode. A/R generally include many medium and low value items and a few high value items. |
|
|
Positively skewed |
Skewed to the right, right tail is longer. Mean > Mode |
|
|
Negatively skewed |
Skewed to the left, left tail is longer. Median > Mean |
|
|
Best estimate of central tendency for asymmetrical distributions? |
Median - not biased by extremes |
|
|
Population's Variability |
The extent to which the values of items are spread about the mean |
|
|
How is population variability measured |
Standard deviation |
|
|
Area under the Standard Deviation Curve |
Confidence Level |
|
|
Confidence Level |
Reliability
Percentage of times that a sample is expected to be representative of the population - i.e., a confidence level of 95% should result in representative samples 95% of the time |
|
|
Confidence Interval |
Precision
Allowance for sampling risk, based on a specified confidence level is the range around a sample value that is expected to contain the true population value
If repeated random samples are drawn from a normally distributed population and the auditor specifies a 95% confidence level the probability is that 95% of the confidence intervals constructed around the samples will contain the population value |
|
|
What does the size of the confidence interval depend on |
The sample size
The larger the sample size, the smaller the confidence interval can be
A smaller confidence interval means that the precision of the sample is greater and the true population value is expected to be in the narrower range around the sample value |
|
|
Standard error of mean |
Standard deviation of the distribution of sample means |
|
|
What is the standard error used for? |
Compute precision (the confidence interval). The larger the standard error the wider the interval |
|
|
Coefficient of variability |
Measures the relative variability within the data and is calculated by dividing the standard deviation of the sample by the mean |
|
|
Nonstatistical/Judgmental Sampling |
Uses the subjective judgment to determine the sample size and selection |
|
|
Advantages of judgmental sampling |
Can be less expensive and time consuming, not special knowledge of statistics and no special statistics software is needed
Greater discretion to use judgment and expertise - with substantial experience, no time is wasted on testing immaterial items |
|
|
Disadvantages of judgmental sampling |
Does not provide a quantitative of sampling risk
Does not provide a quantitative expression of sample risks
Is the auditor is not proficient, the sample may not be effective |
|
|
What does statistical sampling proide |
An objective method of determining sample size and selecting the items to be examined
Provides means of quantitatively assessing precision (how closely the sample represents the population) and confidence level (the percentage of time the sample will adequately represent the population) |
|
|
Advantages of statistical sampling |
Provides a quantitative measure of sampling risk confidence level, and precision
Provides a quantitative expression of sample results
Helps the auditor to design an efficient sample |
|
|
Disadvantages of statistical sampling |
Can be more expensive and time consuming
Requires special statistical knowledge and training
Requires statistical software |
|
|
Nonsampling Risk |
Audit risk not related to sampling - i.e., failure to detect and error in the sample |
|
|
Sampling Risk |
Risk that a sample is not representative of the population, which may result in an incorrect conclusion |
|
|
How is sampling risk related to sample size? |
Inverse relation - as the sample increases, sampling risk decreases |
|
|
Methods of sampling a population |
Random sample
Systemic (interval) sampling
Cluster (block) sampling |
|
|
Random sample |
Every item in a population has an equal and nonzero chance of being selected |
|
|
Traditional means of ensuring randomness |
Assign a random number to each item using random number tables |
|
|
What does a systemic (interval) sampling assume |
That items are arranged randomly in the population |
|
|
What does systemic sampling involve |
Dividing the population by the sample size and selecting every nth item after a random start in the first interval |
|
|
Cluster (block) sampling |
Randomly selects groups of items as the sampling units rather than individual items |
|
|
Advantage of cluster sampling |
Avoids the need to assign random numbers to individual items in the population. Instead, clusters are randomly selected |
|
|
Possible disadvantage of cluster sampling |
Variability of items within the clusters may not be representative of the variability within the population |
|
|
Attribute sampling |
Each item in the population has an attribute of interest |
|
|
Uses of attribute sampling |
Appropriate for tests of controls - i.e., when two outcomes are possible - compliance/noncompliance |
|
|
Factors in determining sample size for an attribute test |
Confidence level - percentage of times that a sample is expected to be representative of the population. The greater the desired confidence level the larger the sample size should be
Population size - the larger the population the larger the sample size
Expected deviation rate - the greater the population deviation, the larger the sample size
Tolerable deviation rate - highest allowable percentage of the population that can be in error and still allow reliance on the tested control. The lower the tolerable deviation rate the larger the sample size |
|
|
Audit Effectiveness |
The degree to which a particular engagement step helps to achieve one or more engagement objectives |
|
|
Determining precision in attribute sampling |
Precision = Tolerable deviation rate - expected deviation rate |
|
|
Relation between precision and sample size in attribute sampling |
Inversely related - as the required precision decreases (tightens), the sample size must increase |
|
|
Two other attribute sampling methods |
Discovery sampling
Stop or go sampling |
|
|
Discovery sampling |
Appropriate when even a single deviation is critical
Occurrence rate assumed to be at or near 0%, and the method cannot be used to evaluate results statistically if deviations are found
Sample size calculated so that it will include at least once instance of a deviation if deviations occur in the population at a given rate |
|
|
Stop or go sampling |
aka Sequential sampling - reduce the sample size when the auditor believes the error rate is low
Examines only enough sample items to be able to state that the deviation rate is below a specified rate at a specified level of confidence
Because the sample size is not fixed, auditor can achieve the desired, result, even if deviations are found by enlarging the sample sufficiency |
|
|
Relation between expected deviation rate and sample size in attribute testing |
Directly related - if expected deviation rate decreases sample size will decrease |
|
|
What kind of variables is attribute sampling used for? |
Discrete variables |
|
|
What type of sampling is used for continuous variables? |
Variables sampling |
|
|
What are continuous variables |
Weights, monetary amounts, etc |
|
|
What does variables sampling provide |
Information about whether a stated amount is matterially missated |
|
|
4 variables sampling techniques |
Unstratified mean-per-unit
Stratified mean-per-unit
Difference estimation
Ratio estimation |
|
|
Factors in determining sample size for variables testing |
Confidence level - the greater the desired confidence level the larger the sample size
Population size - the larger the population, the larger the sample
Tolerate misstatement - interval around the sample statistic that is expected the include the true balance of the population at the specific level - the narrower the precision, the larger the sample should be
Standard deviation - increase in the estimated standard deviation increases the sample size |
|
|
Variables Sample Size Chart - factors affecting sample size:
Confidence level increase Estimated std deviation increase Population size increase Tolerable misstatement increase |
Confidence level increase = sample size increase
Std deviation increase = sample size increase
Population size increase = sample size increase
Tolerable misstatement increase = sample size decrease |
|
|
Attribute Sample Size Chart - factors affecting sample size:
Confidence level increase Population size increase Expected deviation rate increase Tolerable deviation rate increase |
Confidence level increase = sample size increase Population size increase = sample size increase Std deviation increase = sample size increase
Tolerable misstatement increase = sample size decrease |
