Statistical Sampling and Hypothesis Testing Flashcards
6 cards from real ADA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Statistical Sampling and Hypothesis Testing flashcards as text
In regression analysis used during an audit, the R-squared value indicates:
Answer: The proportion of variance in the dependent variable explained by the independent variable(s)
R-squared measures how well the independent variables explain the variability in the dependent variable, ranging from 0 to 1.
Which sampling approach is most appropriate when an auditor wants to evaluate the rate of control deviations?
Answer: Attribute sampling
Attribute sampling measures the proportion of items that have a specific characteristic, making it ideal for control deviation rate testing.
An auditor applies systematic sampling to a population of 5,000 invoices with a required sample of 100. After selecting a random start of 12, what is the next item selected?
Answer: 112
The sampling interval is 5,000 / 100 = 50, so the next item after 12 is 12 + 50 = 62. Wait — actually 12 + 50 = 62.
When an auditor identifies a statistically significant outlier in transaction data, the most appropriate next step is to:
Answer: Investigate the outlier to determine if it represents an error, fraud, or legitimate exception
Outliers warrant follow-up investigation to understand root cause before reaching a conclusion about their nature.
Which concept describes the maximum rate of control deviations the auditor will tolerate before concluding a control is ineffective?
Answer: Tolerable deviation rate
The tolerable deviation rate is the threshold the auditor sets in advance; if the upper deviation limit exceeds it, the control is deemed unreliable.
A null hypothesis for an analytical procedure states that account balance X equals the prior-year balance. The auditor would reject this hypothesis if:
Answer: The computed test statistic falls within the critical region
Rejecting the null hypothesis requires the test statistic to fall in the critical region, indicating the difference is statistically significant.