WGU C784 -APPLIED HEALTH CARE STATISTICS OBJECTIVE
ASSESSMENT EXAM – QUESTIONS AND ANSWERS | VERIFIED
AND WELL DETAILED ANSWERS PLUS RATIONALES |
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1. A clinical researcher analyzes patient recovery times following a novel cardiac
procedure. The collected dataset shows most patients recover within a tight window of 4 to
6 days, but a small group of patients experienced severe complications, extending recovery
up to 30 days. Which measure of central tendency would best represent the typical
recovery time for a clinical report intended for hospital administration?
A. Mean
B. Median
C. Mode
D. Range
The median is resistant to extreme outliers and skewed data, making it the most appropriate
measure of central tendency when a small subset of patients experiences abnormally long
recovery times. The mean would be artificially inflated by the high values, whereas the mode
only identifies the single most frequent value without accounting for the overall distribution.
2. A nurse manager reviews quarterly readmission rates across four hospital units. Unit A
has a readmission rate of 12% out of 200 discharges; Unit B has 15% out of 100
discharges; Unit C has 10% out of 400 discharges; and Unit D has 20% out of 50
discharges. Which unit had the highest absolute number of readmitted patients?
A. Unit A
B. Unit B
C. Unit C
D. Unit D
To find the absolute number of readmissions, multiply the total discharges by the readmission
percentage for each unit: Unit A has 24 readmissions (200 x 0.12), Unit B has 15 (100 x 0.15),
Unit C has 40 (400 x 0.10), and Unit D has 10 (50 x 0.20). Therefore, Unit C processed the
highest absolute number of readmitted patients despite having the lowest percentage rate.
,3. In a study evaluating a new diagnostic screening tool, a researcher calculates the
probability that a patient tests positive given that they actually have the disease. What
statistical term describes this probability?
A. Specificity
B. Sensitivity
C. Positive predictive value
D. Negative predictive value
Sensitivity measures the conditional probability that a diagnostic test correctly identifies
individuals who truly have the condition (true positive rate). Specificity measures the
proportion of healthy individuals who test negative. Predictive values depend heavily on
disease prevalence in the tested population.
4. A public health analyst categorizes patient triage urgency levels in an emergency
department as Level 1 (Resuscitation), Level 2 (Emergent), Level 3 (Urgent), Level 4 (Less
Urgent), and Level 5 (Non-Urgent). What level of measurement best describes these triage
categories?
A. Nominal
B. Ordinal
C. Interval
D. Ratio
Ordinal data involve categories that possess a meaningful logical order or ranking, but the
mathematical distance between adjacent categories is not necessarily equal or quantifiable.
Nominal data lack order, interval data lack a true zero point, and ratio data possess an
absolute zero.
5. A hospital pharmacy tracks the waiting time for emergency prescription refills. The
distribution of waiting times is heavily right-skewed. If the mean waiting time is 25 minutes
and the median is 18 minutes, what can be inferred about the presence of extreme values?
A. There are several extremely low waiting times pulling the median down.
B. There are several extremely long waiting times pulling the mean up.
C. The distribution is symmetric, indicating normal variability.
D. The data contains errors because mean and median cannot differ in healthcare metrics.
, In a right-skewed distribution, the tail extends toward higher positive values, pulling the
arithmetic mean higher than the median. Extremely low values would pull the mean below the
median, creating a left-skew.
6. A quality improvement team measures patient wait times in minutes across an outpatient
clinic. The data yield a mean of 32 minutes and a standard deviation of 8 minutes.
Assuming a bell-shaped normal distribution, approximately what percentage of patients
wait between 16 and 48 minutes?
A. 68%
B. 95%
C. 99.7%
D. 50%
According to the empirical rule (68-95-99.7 rule) for normal distributions, approximately
95% of data values fall within two standard deviations of the mean. Two standard deviations
below the mean is 16 (32 - 16), and two standard deviations above is 48 (32 + 16).
7. A biostatistician tests a new antihypertensive medication and sets the significance level
(alpha) at 0.05. After completing the analysis, the researcher rejects the null hypothesis,
concluding the drug lowers blood pressure when it actually does not. What type of
statistical error has occurred?
A. Type I error (alpha)
B. Type II error (beta)
C. Sampling error
D. Measurement error
A Type I error occurs when a researcher incorrectly rejects a true null hypothesis, registering
a false positive effect. A Type II error occurs when a researcher fails to reject a false null
hypothesis, missing a real effect. Sampling and measurement errors are distinct from
hypothesis testing error classifications.
8. An epidemiologist analyzes the correlation coefficient ($r$) between daily ambient
temperature and heat-related hospital admissions, finding an $r$ value of +0.88. How
should this statistical relationship be interpreted?
A. Strong negative correlation
B. Weak positive correlation
C. Strong positive correlation
ASSESSMENT EXAM – QUESTIONS AND ANSWERS | VERIFIED
AND WELL DETAILED ANSWERS PLUS RATIONALES |
GUARANTEED PASS | LATEST EXAM UPDATE | EXAM PREP |
STUDY GUIDE | PRACTICE TEST| DOWNLOAD INSTANT PDF
1. A clinical researcher analyzes patient recovery times following a novel cardiac
procedure. The collected dataset shows most patients recover within a tight window of 4 to
6 days, but a small group of patients experienced severe complications, extending recovery
up to 30 days. Which measure of central tendency would best represent the typical
recovery time for a clinical report intended for hospital administration?
A. Mean
B. Median
C. Mode
D. Range
The median is resistant to extreme outliers and skewed data, making it the most appropriate
measure of central tendency when a small subset of patients experiences abnormally long
recovery times. The mean would be artificially inflated by the high values, whereas the mode
only identifies the single most frequent value without accounting for the overall distribution.
2. A nurse manager reviews quarterly readmission rates across four hospital units. Unit A
has a readmission rate of 12% out of 200 discharges; Unit B has 15% out of 100
discharges; Unit C has 10% out of 400 discharges; and Unit D has 20% out of 50
discharges. Which unit had the highest absolute number of readmitted patients?
A. Unit A
B. Unit B
C. Unit C
D. Unit D
To find the absolute number of readmissions, multiply the total discharges by the readmission
percentage for each unit: Unit A has 24 readmissions (200 x 0.12), Unit B has 15 (100 x 0.15),
Unit C has 40 (400 x 0.10), and Unit D has 10 (50 x 0.20). Therefore, Unit C processed the
highest absolute number of readmitted patients despite having the lowest percentage rate.
,3. In a study evaluating a new diagnostic screening tool, a researcher calculates the
probability that a patient tests positive given that they actually have the disease. What
statistical term describes this probability?
A. Specificity
B. Sensitivity
C. Positive predictive value
D. Negative predictive value
Sensitivity measures the conditional probability that a diagnostic test correctly identifies
individuals who truly have the condition (true positive rate). Specificity measures the
proportion of healthy individuals who test negative. Predictive values depend heavily on
disease prevalence in the tested population.
4. A public health analyst categorizes patient triage urgency levels in an emergency
department as Level 1 (Resuscitation), Level 2 (Emergent), Level 3 (Urgent), Level 4 (Less
Urgent), and Level 5 (Non-Urgent). What level of measurement best describes these triage
categories?
A. Nominal
B. Ordinal
C. Interval
D. Ratio
Ordinal data involve categories that possess a meaningful logical order or ranking, but the
mathematical distance between adjacent categories is not necessarily equal or quantifiable.
Nominal data lack order, interval data lack a true zero point, and ratio data possess an
absolute zero.
5. A hospital pharmacy tracks the waiting time for emergency prescription refills. The
distribution of waiting times is heavily right-skewed. If the mean waiting time is 25 minutes
and the median is 18 minutes, what can be inferred about the presence of extreme values?
A. There are several extremely low waiting times pulling the median down.
B. There are several extremely long waiting times pulling the mean up.
C. The distribution is symmetric, indicating normal variability.
D. The data contains errors because mean and median cannot differ in healthcare metrics.
, In a right-skewed distribution, the tail extends toward higher positive values, pulling the
arithmetic mean higher than the median. Extremely low values would pull the mean below the
median, creating a left-skew.
6. A quality improvement team measures patient wait times in minutes across an outpatient
clinic. The data yield a mean of 32 minutes and a standard deviation of 8 minutes.
Assuming a bell-shaped normal distribution, approximately what percentage of patients
wait between 16 and 48 minutes?
A. 68%
B. 95%
C. 99.7%
D. 50%
According to the empirical rule (68-95-99.7 rule) for normal distributions, approximately
95% of data values fall within two standard deviations of the mean. Two standard deviations
below the mean is 16 (32 - 16), and two standard deviations above is 48 (32 + 16).
7. A biostatistician tests a new antihypertensive medication and sets the significance level
(alpha) at 0.05. After completing the analysis, the researcher rejects the null hypothesis,
concluding the drug lowers blood pressure when it actually does not. What type of
statistical error has occurred?
A. Type I error (alpha)
B. Type II error (beta)
C. Sampling error
D. Measurement error
A Type I error occurs when a researcher incorrectly rejects a true null hypothesis, registering
a false positive effect. A Type II error occurs when a researcher fails to reject a false null
hypothesis, missing a real effect. Sampling and measurement errors are distinct from
hypothesis testing error classifications.
8. An epidemiologist analyzes the correlation coefficient ($r$) between daily ambient
temperature and heat-related hospital admissions, finding an $r$ value of +0.88. How
should this statistical relationship be interpreted?
A. Strong negative correlation
B. Weak positive correlation
C. Strong positive correlation
