D 514 Exam 2 V2 | D 514 Analytical Methods of
Healthcare Leaders | Actual Q&A with Rationale
(D514 Exam 2) | Western Governors University
1. When a healthcare analyst calculates a p-value of 0.03 for a study comparing two patient
safety protocols with a significance level (alpha) of 0.05, what is the appropriate statistical
conclusion?
A. Accept the null hypothesis as there is no difference.
B. Fail to reject the null hypothesis due to insufficient evidence.
C. Reject the null hypothesis, indicating the results are statistically significant.
D. Conclude that the p-value is too high to make a determination.
Correct Answer: C
Explanation: The p-value represents the probability of obtaining the observed results if
the null hypothesis were true. Since 0.03 is less than the alpha level of 0.05, the analyst
must reject the null hypothesis. This decision indicates that there is a statistically
significant difference between the two protocols evaluated.
2. Which type of error is committed when a healthcare leader concludes that a new discharge
process significantly reduces readmission rates, when in reality, the change was due to
random chance?
A. Type I Error
B. Type II Error
,C. Standard Error of Measurement
D. Selection Bias
Correct Answer: A
Explanation: A Type I error occurs when the null hypothesis is rejected even though it is
actually true, often referred to as a ‘false positive.’ In this scenario, the leader incorrectly
identifies an effect that does not exist. Controlling this error is vital for ensuring that
healthcare resources are not wasted on ineffective interventions.
3. An analyst is examining the relationship between nurse staffing levels and patient falls. If
the R-squared value is 0.85, how should this result be interpreted?
A. There is an 85% chance that staffing levels cause falls.
B. Staffing levels explain 85% of the variation in patient falls.
C. The correlation between staffing and falls is exactly 0.85.
D. The model is only accurate for 85% of the patients sampled.
Correct Answer: B
Explanation: The R-squared value, or the coefficient of determination, represents the
proportion of variance in the dependent variable that is predictable from the independent
variable. An R-squared of 0.85 indicates that 85% of the variability in patient falls is
explained by the nurse staffing levels. The remaining 15% is attributed to other factors or
random noise not included in the model.
, 4. In a normal distribution of patient wait times, what percentage of the data points are
expected to fall within two standard deviations of the mean?
A. 95%
B. 90%
C. 68%
D. 99.7%
Correct Answer: A
Explanation: According to the Empirical Rule (68-95-99.7 rule) for normal distributions,
approximately 95% of data falls within two standard deviations of the mean. This
statistical property is fundamental for establishing control limits in healthcare quality
monitoring. Understanding these percentages helps leaders identify which data points
represent common cause variation versus outliers.
5. A healthcare facility utilizes a Control Chart to monitor surgical site infections. What does a
data point falling outside the Lower Control Limit (LCL) usually indicate?
A. A special cause variation is present, potentially indicating a process improvement.
B. The process is stable and performing as expected.
C. A Type II error has occurred in the measurement process.
D. The infection rate has reached a dangerous level and requires immediate correction.
Correct Answer: A
Healthcare Leaders | Actual Q&A with Rationale
(D514 Exam 2) | Western Governors University
1. When a healthcare analyst calculates a p-value of 0.03 for a study comparing two patient
safety protocols with a significance level (alpha) of 0.05, what is the appropriate statistical
conclusion?
A. Accept the null hypothesis as there is no difference.
B. Fail to reject the null hypothesis due to insufficient evidence.
C. Reject the null hypothesis, indicating the results are statistically significant.
D. Conclude that the p-value is too high to make a determination.
Correct Answer: C
Explanation: The p-value represents the probability of obtaining the observed results if
the null hypothesis were true. Since 0.03 is less than the alpha level of 0.05, the analyst
must reject the null hypothesis. This decision indicates that there is a statistically
significant difference between the two protocols evaluated.
2. Which type of error is committed when a healthcare leader concludes that a new discharge
process significantly reduces readmission rates, when in reality, the change was due to
random chance?
A. Type I Error
B. Type II Error
,C. Standard Error of Measurement
D. Selection Bias
Correct Answer: A
Explanation: A Type I error occurs when the null hypothesis is rejected even though it is
actually true, often referred to as a ‘false positive.’ In this scenario, the leader incorrectly
identifies an effect that does not exist. Controlling this error is vital for ensuring that
healthcare resources are not wasted on ineffective interventions.
3. An analyst is examining the relationship between nurse staffing levels and patient falls. If
the R-squared value is 0.85, how should this result be interpreted?
A. There is an 85% chance that staffing levels cause falls.
B. Staffing levels explain 85% of the variation in patient falls.
C. The correlation between staffing and falls is exactly 0.85.
D. The model is only accurate for 85% of the patients sampled.
Correct Answer: B
Explanation: The R-squared value, or the coefficient of determination, represents the
proportion of variance in the dependent variable that is predictable from the independent
variable. An R-squared of 0.85 indicates that 85% of the variability in patient falls is
explained by the nurse staffing levels. The remaining 15% is attributed to other factors or
random noise not included in the model.
, 4. In a normal distribution of patient wait times, what percentage of the data points are
expected to fall within two standard deviations of the mean?
A. 95%
B. 90%
C. 68%
D. 99.7%
Correct Answer: A
Explanation: According to the Empirical Rule (68-95-99.7 rule) for normal distributions,
approximately 95% of data falls within two standard deviations of the mean. This
statistical property is fundamental for establishing control limits in healthcare quality
monitoring. Understanding these percentages helps leaders identify which data points
represent common cause variation versus outliers.
5. A healthcare facility utilizes a Control Chart to monitor surgical site infections. What does a
data point falling outside the Lower Control Limit (LCL) usually indicate?
A. A special cause variation is present, potentially indicating a process improvement.
B. The process is stable and performing as expected.
C. A Type II error has occurred in the measurement process.
D. The infection rate has reached a dangerous level and requires immediate correction.
Correct Answer: A
