C785 Applied Healthcare Statistics: Hypothesis Testing & p-Values
Practice Pack 2026 |WGU
1. Which of the following best defines the null hypothesis (H0)?
A. A statement of no effect or no difference between groups
B. A statement that there is a significant effect in the population
C. The probability that the observed results occurred by chance
D. The range of values likely to contain the population mean
Answer: A
Rationale: The null hypothesis (H0) assumes that any observed difference is due to
sampling error or chance, meaning no real effect exists.
2. If a researcher calculates a p-value of 0.02 and the alpha level is 0.05, what is
the correct decision?
A. Fail to reject the null hypothesis
B. Reject the null hypothesis
C. Accept the null hypothesis as true
D. Increase the sample size and re-test
Answer: B
Rationale: Since the p-value (0.02) is less than the significance level (0.05), the researcher
rejects the null hypothesis in favor of the alternative.
,3. A Type I error occurs when:
A. The null hypothesis is true, but we reject it
B. The null hypothesis is false, but we fail to reject it
C. The alternative hypothesis is true, and we accept it
D. The p-value is greater than the alpha level
Answer: A
Rationale: A Type I error is a ‘false positive,’ where we incorrectly conclude an effect exists
when the null hypothesis is actually true.
4. What does a p-value represent in hypothesis testing?
A. The probability of observing the results if the null hypothesis is true
B. The probability that the null hypothesis is absolutely true
C. The probability that the alternative hypothesis is false
D. The fixed level of significance set by the researcher
Answer: A
Rationale: The p-value measures the probability of obtaining test results at least as
extreme as the results actually observed, under the assumption that the null hypothesis is
correct.
5. A Type II error occurs when:
A. The researcher rejects a true null hypothesis
B. The researcher fails to reject a false null hypothesis
C. The p-value is equal to the alpha level
D. The sample size is too large for the test
Answer: B
Rationale: A Type II error is a ‘false negative,’ where the researcher misses a real effect
that actually exists in the population.
, 6. If the alpha level is decreased from 0.05 to 0.01, what happens to the risk of a
Type I error?
A. The risk increases
B. The risk remains the same
C. The risk becomes zero
D. The risk decreases
Answer: D
Rationale: The alpha level is the probability of a Type I error. Reducing alpha directly
reduces the risk of making a Type I error.
7. Which of the following increases the statistical power of a study?
A. Decreasing the sample size
B. Decreasing the alpha level
C. Increasing the sample size
D. Increasing the population standard deviation
Answer: C
Rationale: Increasing sample size reduces standard error, which increases the likelihood
of detecting a real effect, thereby increasing power.
8. In a two-tailed test, how is the rejection region distributed?
A. Split equally between the upper and lower tails
B. Entirely in the lower tail of the distribution
C. Entirely in the upper tail of the distribution
D. Concentrated in the center of the distribution
Answer: A
Rationale: A two-tailed test looks for differences in both directions, so the alpha
(significance level) is divided between both tails.
Practice Pack 2026 |WGU
1. Which of the following best defines the null hypothesis (H0)?
A. A statement of no effect or no difference between groups
B. A statement that there is a significant effect in the population
C. The probability that the observed results occurred by chance
D. The range of values likely to contain the population mean
Answer: A
Rationale: The null hypothesis (H0) assumes that any observed difference is due to
sampling error or chance, meaning no real effect exists.
2. If a researcher calculates a p-value of 0.02 and the alpha level is 0.05, what is
the correct decision?
A. Fail to reject the null hypothesis
B. Reject the null hypothesis
C. Accept the null hypothesis as true
D. Increase the sample size and re-test
Answer: B
Rationale: Since the p-value (0.02) is less than the significance level (0.05), the researcher
rejects the null hypothesis in favor of the alternative.
,3. A Type I error occurs when:
A. The null hypothesis is true, but we reject it
B. The null hypothesis is false, but we fail to reject it
C. The alternative hypothesis is true, and we accept it
D. The p-value is greater than the alpha level
Answer: A
Rationale: A Type I error is a ‘false positive,’ where we incorrectly conclude an effect exists
when the null hypothesis is actually true.
4. What does a p-value represent in hypothesis testing?
A. The probability of observing the results if the null hypothesis is true
B. The probability that the null hypothesis is absolutely true
C. The probability that the alternative hypothesis is false
D. The fixed level of significance set by the researcher
Answer: A
Rationale: The p-value measures the probability of obtaining test results at least as
extreme as the results actually observed, under the assumption that the null hypothesis is
correct.
5. A Type II error occurs when:
A. The researcher rejects a true null hypothesis
B. The researcher fails to reject a false null hypothesis
C. The p-value is equal to the alpha level
D. The sample size is too large for the test
Answer: B
Rationale: A Type II error is a ‘false negative,’ where the researcher misses a real effect
that actually exists in the population.
, 6. If the alpha level is decreased from 0.05 to 0.01, what happens to the risk of a
Type I error?
A. The risk increases
B. The risk remains the same
C. The risk becomes zero
D. The risk decreases
Answer: D
Rationale: The alpha level is the probability of a Type I error. Reducing alpha directly
reduces the risk of making a Type I error.
7. Which of the following increases the statistical power of a study?
A. Decreasing the sample size
B. Decreasing the alpha level
C. Increasing the sample size
D. Increasing the population standard deviation
Answer: C
Rationale: Increasing sample size reduces standard error, which increases the likelihood
of detecting a real effect, thereby increasing power.
8. In a two-tailed test, how is the rejection region distributed?
A. Split equally between the upper and lower tails
B. Entirely in the lower tail of the distribution
C. Entirely in the upper tail of the distribution
D. Concentrated in the center of the distribution
Answer: A
Rationale: A two-tailed test looks for differences in both directions, so the alpha
(significance level) is divided between both tails.
