WGU C784 - APPLIED HEALTHCARE STATISTICS
PRE-ASSESSMENT EXAM QUESTIONS AND VERIFIED
ANSWERS 100% CORRECT - 179 Questions and Answers
Already Graded A+ Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This pre-assessment exam covers key topics in applied healthcare statistics,
including probability, hypothesis testing, confidence intervals, regression, and
ANOVA. It is designed to test advanced conceptual understanding and application
of statistical methods in clinical and public health contexts.
Expected Grade A+
Total Questions 179
Duration 3 hours
Learning Outcomes 1. Apply statistical reasoning to healthcare data
2. Interpret confidence intervals and hypothesis tests
3. Analyze relationships using regression and correlation
4. Evaluate experimental designs and biases
Accreditation Meets US university standards for undergraduate/graduate statistics courses in
healthcare programs
Page 1
,1. In a randomized controlled trial comparing a new antihypertensive drug to
placebo, the primary endpoint is reduction in systolic blood pressure (SBP) at 12
weeks. The study reports a mean difference of 5.2 mmHg (95% CI: 2.1 to 8.3 mmHg,
p=0.001). Which of the following interpretations is MOST consistent with these
results?
A. There is a 95% probability that the true mean difference lies between 2.1 and 8.3 mmHg.
B. The null hypothesis of no difference is rejected, and the effect size is clinically
meaningful.
C. The confidence interval indicates that the drug is superior to placebo with 95% certainty.
D. The p-value suggests a 0.1% chance that the observed difference is due to chance alone.
Answer: D. The p-value suggests a 0.1% chance that the observed difference is due
to chance alone.
The p-value of 0.001 means that if the null hypothesis were true (no difference), the
probability of observing a difference as extreme as 5.2 mmHg is 0.1%. Option A is
incorrect because the CI does not give a probability about the parameter. Option B is
subjective; clinical meaningfulness is not determined by statistics alone. Option C
misinterprets the CI as a probability of superiority.
2. A researcher is analyzing the relationship between body mass index (BMI) and
fasting glucose levels in a cohort of 500 adults. A simple linear regression yields a
slope of 0.8 (p<0.001) and an R-squared of 0.25. Which of the following is the MOST
appropriate conclusion?
A. A one-unit increase in BMI is associated with a 0.8 mg/dL increase in fasting glucose,
and BMI explains 25% of the variability in glucose.
B. A one-unit increase in BMI causes a 0.8 mg/dL increase in fasting glucose, and the model
fits well.
C. The correlation coefficient is 0.5, indicating a moderate positive linear relationship.
D. The residual plot should show a random pattern; if not, the regression is invalid.
Answer: A. A one-unit increase in BMI is associated with a 0.8 mg/dL increase in
fasting glucose, and BMI explains 25% of the variability in glucose.
The slope indicates the change in glucose per unit increase in BMI, and R-squared
shows the proportion of variance explained. Option B incorrectly implies causation.
Option C is true but not the most appropriate conclusion from the given information.
Option D is a diagnostic step, not a conclusion.
Page 2
,3. A study reports a relative risk (RR) of 1.8 for developing lung cancer among
smokers compared to non-smokers, with a 95% confidence interval of 1.3 to 2.5.
Which of the following statements is CORRECT?
A. Smokers have an 80% increased risk of lung cancer compared to non-smokers.
B. The risk difference is 0.8.
C. The p-value for the null hypothesis RR=1 is greater than 0.05.
D. The absolute risk reduction is 0.5.
Answer: A. Smokers have an 80% increased risk of lung cancer compared to
non-smokers.
RR=1.8 means the risk in smokers is 1.8 times that in non-smokers, which is an 80%
increase. Option B is incorrect because risk difference is not given. Option C is false
because the CI excludes 1, so p<0.05. Option D is not derivable from the RR alone.
4. A hospital wants to test if a new hand hygiene protocol reduces infection rates.
They compare infection rates before and after implementation in the same unit.
Which statistical test is MOST appropriate?
A. Independent samples t-test
B. Paired t-test
C. Chi-square test of independence
D. One-sample z-test for proportions
Answer: B. Paired t-test
The data are paired (same unit before/after), so a paired t-test is appropriate for
comparing means. Option A is for independent groups. Option C is for categorical
associations. Option D would compare a proportion to a known value, not paired data.
5. A diagnostic test for a disease has a sensitivity of 95% and specificity of 90%. The
prevalence of the disease in the population is 2%. What is the probability that a
person with a positive test actually has the disease (positive predictive value)?
A. 0.162
B. 0.019
C. 0.950
D. 0.900
Answer: A. 0.162
PPV = (sensitivity * prevalence) / (sensitivity * prevalence +
(1-specificity)*(1-prevalence)) = (0.95*0.02)/(0.95*0.02+0.10*0.98)=0.019/0.117=0.162.
Option B is the numerator. Options C and D are sensitivity and specificity.
Page 3
, 6. In an ANOVA comparing mean cholesterol levels across four diet groups, the
F-statistic is 3.45 with a p-value of 0.02. Which of the following is TRUE?
A. At least one group mean is significantly different from the others at =0.05.
B. All four group means are significantly different from each other.
C. The null hypothesis that all means are equal cannot be rejected.
D. A post-hoc test is unnecessary because the overall test is significant.
Answer: A. At least one group mean is significantly different from the others at
=0.05.
The significant F-test indicates that not all group means are equal, but does not specify
which pairs differ. Option B is too strong. Option C is false. Option D is incorrect
because post-hoc tests are needed to identify specific differences.
7. A researcher wants to estimate the mean hemoglobin level in a population with a
margin of error of 0.5 g/dL and 95% confidence. The standard deviation is estimated
as 2.0 g/dL. What is the minimum sample size required?
A. 62
B. 64
C. 16
D. 31
Answer: A. 62
n = (Z*/E)^2 = (1.96*2/0.5)^2 = (7.84)^2 = 61.47, round up to 62. Option B (64) uses
Z=2. Option C uses half the SD. Option D is half of 62.
8. A study examines the correlation between hours of sleep and cognitive test scores
in 100 adults. The Pearson correlation coefficient is 0.30 (p=0.002). Which of the
following is the MOST accurate interpretation?
A. There is a weak but statistically significant positive linear relationship between sleep and
cognitive scores.
B. Sleep causes a 30% increase in cognitive scores.
C. The coefficient of determination is 0.09, indicating that sleep explains 9% of the variance
in cognitive scores.
D. Both A and C are correct.
Answer: D. Both A and C are correct.
A correlation of 0.30 is weak to moderate, and the p-value indicates significance.
R-squared = 0.09, meaning 9% of variance explained. Option B implies causation,
which is not supported. Thus D is correct.
Page 4
PRE-ASSESSMENT EXAM QUESTIONS AND VERIFIED
ANSWERS 100% CORRECT - 179 Questions and Answers
Already Graded A+ Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This pre-assessment exam covers key topics in applied healthcare statistics,
including probability, hypothesis testing, confidence intervals, regression, and
ANOVA. It is designed to test advanced conceptual understanding and application
of statistical methods in clinical and public health contexts.
Expected Grade A+
Total Questions 179
Duration 3 hours
Learning Outcomes 1. Apply statistical reasoning to healthcare data
2. Interpret confidence intervals and hypothesis tests
3. Analyze relationships using regression and correlation
4. Evaluate experimental designs and biases
Accreditation Meets US university standards for undergraduate/graduate statistics courses in
healthcare programs
Page 1
,1. In a randomized controlled trial comparing a new antihypertensive drug to
placebo, the primary endpoint is reduction in systolic blood pressure (SBP) at 12
weeks. The study reports a mean difference of 5.2 mmHg (95% CI: 2.1 to 8.3 mmHg,
p=0.001). Which of the following interpretations is MOST consistent with these
results?
A. There is a 95% probability that the true mean difference lies between 2.1 and 8.3 mmHg.
B. The null hypothesis of no difference is rejected, and the effect size is clinically
meaningful.
C. The confidence interval indicates that the drug is superior to placebo with 95% certainty.
D. The p-value suggests a 0.1% chance that the observed difference is due to chance alone.
Answer: D. The p-value suggests a 0.1% chance that the observed difference is due
to chance alone.
The p-value of 0.001 means that if the null hypothesis were true (no difference), the
probability of observing a difference as extreme as 5.2 mmHg is 0.1%. Option A is
incorrect because the CI does not give a probability about the parameter. Option B is
subjective; clinical meaningfulness is not determined by statistics alone. Option C
misinterprets the CI as a probability of superiority.
2. A researcher is analyzing the relationship between body mass index (BMI) and
fasting glucose levels in a cohort of 500 adults. A simple linear regression yields a
slope of 0.8 (p<0.001) and an R-squared of 0.25. Which of the following is the MOST
appropriate conclusion?
A. A one-unit increase in BMI is associated with a 0.8 mg/dL increase in fasting glucose,
and BMI explains 25% of the variability in glucose.
B. A one-unit increase in BMI causes a 0.8 mg/dL increase in fasting glucose, and the model
fits well.
C. The correlation coefficient is 0.5, indicating a moderate positive linear relationship.
D. The residual plot should show a random pattern; if not, the regression is invalid.
Answer: A. A one-unit increase in BMI is associated with a 0.8 mg/dL increase in
fasting glucose, and BMI explains 25% of the variability in glucose.
The slope indicates the change in glucose per unit increase in BMI, and R-squared
shows the proportion of variance explained. Option B incorrectly implies causation.
Option C is true but not the most appropriate conclusion from the given information.
Option D is a diagnostic step, not a conclusion.
Page 2
,3. A study reports a relative risk (RR) of 1.8 for developing lung cancer among
smokers compared to non-smokers, with a 95% confidence interval of 1.3 to 2.5.
Which of the following statements is CORRECT?
A. Smokers have an 80% increased risk of lung cancer compared to non-smokers.
B. The risk difference is 0.8.
C. The p-value for the null hypothesis RR=1 is greater than 0.05.
D. The absolute risk reduction is 0.5.
Answer: A. Smokers have an 80% increased risk of lung cancer compared to
non-smokers.
RR=1.8 means the risk in smokers is 1.8 times that in non-smokers, which is an 80%
increase. Option B is incorrect because risk difference is not given. Option C is false
because the CI excludes 1, so p<0.05. Option D is not derivable from the RR alone.
4. A hospital wants to test if a new hand hygiene protocol reduces infection rates.
They compare infection rates before and after implementation in the same unit.
Which statistical test is MOST appropriate?
A. Independent samples t-test
B. Paired t-test
C. Chi-square test of independence
D. One-sample z-test for proportions
Answer: B. Paired t-test
The data are paired (same unit before/after), so a paired t-test is appropriate for
comparing means. Option A is for independent groups. Option C is for categorical
associations. Option D would compare a proportion to a known value, not paired data.
5. A diagnostic test for a disease has a sensitivity of 95% and specificity of 90%. The
prevalence of the disease in the population is 2%. What is the probability that a
person with a positive test actually has the disease (positive predictive value)?
A. 0.162
B. 0.019
C. 0.950
D. 0.900
Answer: A. 0.162
PPV = (sensitivity * prevalence) / (sensitivity * prevalence +
(1-specificity)*(1-prevalence)) = (0.95*0.02)/(0.95*0.02+0.10*0.98)=0.019/0.117=0.162.
Option B is the numerator. Options C and D are sensitivity and specificity.
Page 3
, 6. In an ANOVA comparing mean cholesterol levels across four diet groups, the
F-statistic is 3.45 with a p-value of 0.02. Which of the following is TRUE?
A. At least one group mean is significantly different from the others at =0.05.
B. All four group means are significantly different from each other.
C. The null hypothesis that all means are equal cannot be rejected.
D. A post-hoc test is unnecessary because the overall test is significant.
Answer: A. At least one group mean is significantly different from the others at
=0.05.
The significant F-test indicates that not all group means are equal, but does not specify
which pairs differ. Option B is too strong. Option C is false. Option D is incorrect
because post-hoc tests are needed to identify specific differences.
7. A researcher wants to estimate the mean hemoglobin level in a population with a
margin of error of 0.5 g/dL and 95% confidence. The standard deviation is estimated
as 2.0 g/dL. What is the minimum sample size required?
A. 62
B. 64
C. 16
D. 31
Answer: A. 62
n = (Z*/E)^2 = (1.96*2/0.5)^2 = (7.84)^2 = 61.47, round up to 62. Option B (64) uses
Z=2. Option C uses half the SD. Option D is half of 62.
8. A study examines the correlation between hours of sleep and cognitive test scores
in 100 adults. The Pearson correlation coefficient is 0.30 (p=0.002). Which of the
following is the MOST accurate interpretation?
A. There is a weak but statistically significant positive linear relationship between sleep and
cognitive scores.
B. Sleep causes a 30% increase in cognitive scores.
C. The coefficient of determination is 0.09, indicating that sleep explains 9% of the variance
in cognitive scores.
D. Both A and C are correct.
Answer: D. Both A and C are correct.
A correlation of 0.30 is weak to moderate, and the p-value indicates significance.
R-squared = 0.09, meaning 9% of variance explained. Option B implies causation,
which is not supported. Thus D is correct.
Page 4
