WGU C784 APPLIED HEALTHCARE STATISTICS OA
(OBJECTIVE ASSESSMENT) QUESTIONS AND CORRECT
ANSWERS PLUS RATIONALE LATEST 2026-2027 UPDATE
46 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Select and compute appropriate descriptive and inferential statistics for healthcare datasets
2 Interpret p-values, confidence intervals, and effect sizes in the context of clinical decision-making
3 Evaluate the validity and clinical significance of published healthcare research using statistical reasoning
4 WGU C784 Applied Healthcare Statistics OA
5 Objective Assessment
6 Questions and Correct Answers Plus Rationale Latest 2026
7 2027 Update
8 Foundations of Applied Healthcare Statistics
9 Applied Applied Healthcare Statistics
10 Advanced Applied Healthcare Statistics
11 Applied Healthcare Statistics Review
ABSTRACT
This study document brings together 46 carefully worded exam questions drawn from WGU C784
Applied Healthcare Statistics OA (Objective Assessment) Questions and Correct Answers Plus
Rationale Latest 2026-2027 Update, with the strongest emphasis placed on Select and compute
appropriate descriptive and inferential statistics for healthcare datasets, Interpret p-values,
confidence intervals, and effect sizes in the context of clinical decision-making and Evaluate the
validity and clinical significance of published healthcare research using statistical reasoning. Every
item follows the wording style and level of reasoning you meet in the real paper, and each one is
paired with a clear rationale so the correct choice is never a guess. Work through the set at your
own pace, mark the questions that slow you down, then come back to them until the reasoning
feels automatic. Learners who revise this way walk into the exam room recognising the pattern
behind the questions instead of meeting them for the first time. Keep going - steady, honest
practice is what turns a difficult paper into a comfortable pass.
Page 1
,Q1 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
A study reports a 95% confidence interval for the difference in mean systolic blood
pressure between two antihypertensive regimens as (2.1 mmHg, 8.7 mmHg).
Which conclusion is most defensible?
A. There is no statistically significant difference because the interval excludes zero.
B. The difference is statistically significant, but the clinical importance depends on the minimally
important difference. CORRECT
C. The probability that the true difference is zero is 95%.
D. The new regimen lowers blood pressure by exactly 5.4 mmHg in all patients.
RATIONALE: A CI excluding zero indicates statistical significance, but clinical relevance requires
comparison to a known minimally important difference. Option A is false because excluding zero
indicates significance. Option C misinterprets the CI as a posterior probability. Option D confuses
the point estimate with individual treatment effects.
Q2 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
In a dataset of hospital lengths of stay, the mean is 7.2 days and the median is 4.1
days. Which distributional characteristic best explains this difference?
A. The distribution is symmetric with low variance.
B. The distribution is left-skewed with a ceiling effect.
C. The distribution is right-skewed due to high-cost outliers. CORRECT
D. The distribution is bimodal with equal peaks.
RATIONALE: When the mean exceeds the median, the distribution is right-skewed, often due to
a few very long stays. A symmetric distribution would have mean median. Left-skew would show
mean < median. Bimodality does not inherently produce a mean-median gap of this pattern.
Page 2
, Q3 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
A screening test for a rare disease has sensitivity 95% and specificity 90%. In a
population where prevalence is 1%, what is the approximate positive predictive
value (PPV)?
A. 95%
B. 50%
C. 9% CORRECT
D. 1%
RATIONALE: PPV = (sensitivity × prevalence) / [(sensitivity × prevalence) + ((1 - specificity) × (1 -
prevalence))] = (0.95 × 0.01) / [(0.95 × 0.01) + (0.10 × 0.99)] 0..1085 8.8%, or about
9%. High sensitivity does not ensure high PPV when prevalence is low. Options A and B ignore
prevalence, and D confuses PPV with prevalence.
Q4 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
Which statement correctly distinguishes a Type I error from a Type II error in
hypothesis testing?
A. Type I error is failing to reject a false null hypothesis; Type II error is rejecting a true null
hypothesis.
B. Type I error is rejecting a true null hypothesis; Type II error is failing to reject a false null
hypothesis. CORRECT
C. Type I error is the probability of a false positive; Type II error is the probability of a true
positive.
D. Type I error decreases as alpha increases; Type II error is unrelated to power.
RATIONALE: A Type I error (alpha) is rejecting a true null hypothesis (false positive). A Type II
error (beta) is failing to reject a false null hypothesis (false negative). Option A reverses the
definitions. Option C misstates Type II as true positive. Option D incorrectly describes the
relationship between alpha and Type I error.
Page 3
(OBJECTIVE ASSESSMENT) QUESTIONS AND CORRECT
ANSWERS PLUS RATIONALE LATEST 2026-2027 UPDATE
46 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Select and compute appropriate descriptive and inferential statistics for healthcare datasets
2 Interpret p-values, confidence intervals, and effect sizes in the context of clinical decision-making
3 Evaluate the validity and clinical significance of published healthcare research using statistical reasoning
4 WGU C784 Applied Healthcare Statistics OA
5 Objective Assessment
6 Questions and Correct Answers Plus Rationale Latest 2026
7 2027 Update
8 Foundations of Applied Healthcare Statistics
9 Applied Applied Healthcare Statistics
10 Advanced Applied Healthcare Statistics
11 Applied Healthcare Statistics Review
ABSTRACT
This study document brings together 46 carefully worded exam questions drawn from WGU C784
Applied Healthcare Statistics OA (Objective Assessment) Questions and Correct Answers Plus
Rationale Latest 2026-2027 Update, with the strongest emphasis placed on Select and compute
appropriate descriptive and inferential statistics for healthcare datasets, Interpret p-values,
confidence intervals, and effect sizes in the context of clinical decision-making and Evaluate the
validity and clinical significance of published healthcare research using statistical reasoning. Every
item follows the wording style and level of reasoning you meet in the real paper, and each one is
paired with a clear rationale so the correct choice is never a guess. Work through the set at your
own pace, mark the questions that slow you down, then come back to them until the reasoning
feels automatic. Learners who revise this way walk into the exam room recognising the pattern
behind the questions instead of meeting them for the first time. Keep going - steady, honest
practice is what turns a difficult paper into a comfortable pass.
Page 1
,Q1 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
A study reports a 95% confidence interval for the difference in mean systolic blood
pressure between two antihypertensive regimens as (2.1 mmHg, 8.7 mmHg).
Which conclusion is most defensible?
A. There is no statistically significant difference because the interval excludes zero.
B. The difference is statistically significant, but the clinical importance depends on the minimally
important difference. CORRECT
C. The probability that the true difference is zero is 95%.
D. The new regimen lowers blood pressure by exactly 5.4 mmHg in all patients.
RATIONALE: A CI excluding zero indicates statistical significance, but clinical relevance requires
comparison to a known minimally important difference. Option A is false because excluding zero
indicates significance. Option C misinterprets the CI as a posterior probability. Option D confuses
the point estimate with individual treatment effects.
Q2 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
In a dataset of hospital lengths of stay, the mean is 7.2 days and the median is 4.1
days. Which distributional characteristic best explains this difference?
A. The distribution is symmetric with low variance.
B. The distribution is left-skewed with a ceiling effect.
C. The distribution is right-skewed due to high-cost outliers. CORRECT
D. The distribution is bimodal with equal peaks.
RATIONALE: When the mean exceeds the median, the distribution is right-skewed, often due to
a few very long stays. A symmetric distribution would have mean median. Left-skew would show
mean < median. Bimodality does not inherently produce a mean-median gap of this pattern.
Page 2
, Q3 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
A screening test for a rare disease has sensitivity 95% and specificity 90%. In a
population where prevalence is 1%, what is the approximate positive predictive
value (PPV)?
A. 95%
B. 50%
C. 9% CORRECT
D. 1%
RATIONALE: PPV = (sensitivity × prevalence) / [(sensitivity × prevalence) + ((1 - specificity) × (1 -
prevalence))] = (0.95 × 0.01) / [(0.95 × 0.01) + (0.10 × 0.99)] 0..1085 8.8%, or about
9%. High sensitivity does not ensure high PPV when prevalence is low. Options A and B ignore
prevalence, and D confuses PPV with prevalence.
Q4 SELECT AND COMPUTE APPROPRIATE DESCRIPTIVE AND INFERENTIAL STATISTICS
FOR HEALTHCARE DATASETS
Which statement correctly distinguishes a Type I error from a Type II error in
hypothesis testing?
A. Type I error is failing to reject a false null hypothesis; Type II error is rejecting a true null
hypothesis.
B. Type I error is rejecting a true null hypothesis; Type II error is failing to reject a false null
hypothesis. CORRECT
C. Type I error is the probability of a false positive; Type II error is the probability of a true
positive.
D. Type I error decreases as alpha increases; Type II error is unrelated to power.
RATIONALE: A Type I error (alpha) is rejecting a true null hypothesis (false positive). A Type II
error (beta) is failing to reject a false null hypothesis (false negative). Option A reverses the
definitions. Option C misstates Type II as true positive. Option D incorrectly describes the
relationship between alpha and Type I error.
Page 3
