Lecture 3 mcqs

Question 1

1. A study comparing two mouth rinses finds a p-value of 0.02. What does this mean?
   A) The null hypothesis is definitely false
   B) There is a 2% chance the null hypothesis is true
   C) There is a 2% chance of seeing this result (or more extreme) if the null hypothesis is true
   D) The treatment has no effect

Answer 1

Answer: C

Question 2

2. In hypothesis testing, the null hypothesis typically represents:
   A) The effect we hope to prove
   B) A clinically important effect
   C) No difference or no effect
   D) The alternative explanation

Answer 2

C

Question 3

3. A p-value of 0.07 means:
   A) You must reject the null hypothesis
   B) The effect is statistically significant
   C) The result is definitely due to chance
   D) There is not enough evidence to reject the null hypothesis at the 5% level

Answer 3

D

Question 4

4. In a study with low statistical power, you are more likely to:
   A) Make a Type I error
   B) Detect an effect that isn’t there
   C) Miss a real effect
   D) Have a large sample size

Answer 4

c

Question 5

5. You conduct a study with 50 patients per group. You find a 5% difference in recovery rates between two drugs, but the p-value is 0.12. What is the most appropriate conclusion?
   A) The drugs are equally effective
   B) There is not enough evidence to reject the null hypothesis
   C) The study proves no difference exists
   D) The difference is statistically significant

Answer 5

B

Question 6

6. The p-value from a hypothesis test tells us:
   A) The size of the effect
   B) The variability of the data
   C) The probability of the data given the null hypothesis
   D) The likelihood that the treatment caused the result

Answer 6

C

Question 7

7. A Type I error occurs when:
   A) You fail to detect a real effect
   B) You conclude an effect exists when it actually does not
   C) Your sample size is too small
   D) You reject both hypotheses

Answer 7

B

Question 8

8. A dental study aims to detect a difference in plaque scores of 5 points between two methods. What is this 5-point difference called in sample size calculations?
   A) Standard deviation
   B) Power
   C) Effect size
   D) P-value

Answer 8

C

Question 9

9. Which of the following will NOT increase statistical power?
   A) Increasing sample size
   B) Increasing the significance level
   C) Reducing measurement error
   D) Increasing the standard deviation

Answer 9

D

Question 10

10. You want a study with 80% power and α = 0.05. What does the 80% represent?
    A) Type I error
    B) Probability of detecting a true effect
    C) Standard deviation
    D) The margin of error

Answer 10

B

Question 11

11. Which of the following best defines standard error?
    A) A measure of the spread in the population
    B) The standard deviation of the sample
    C) The variability of a sample statistic
    D) The mean squared deviation

Answer 11

C

Question 12

12. If a sample size calculator asks for σ (sigma), what is it referring to?
    A) Standard error
    B) Variance
    C) Standard deviation
    D) Sample mean

Answer 12

C

Question 13

12. If a sample size calculator asks for σ (sigma), what is it referring to?
    A) Sample mean
    B) Standard deviation
    C) Variance
    D) Standard error

Answer 13

B

Question 14

13. A study with a very large sample finds a tiny difference in outcomes and a p-value < 0.001. This result is:
    A) Statistically significant, but may not be clinically important
    B) Clinically important
    C) Not statistically significant
    D) Statistically and clinically significant

Answer 14

A

Question 15

14. A p-value of 0.001 means:
    A) The null hypothesis is false
    B) The result is likely due to chance
    C) The observed data are very unlikely under the null hypothesis
    D) You must accept the null hypothesis

Answer 15

C

Question 16

5. If a study finds no statistically significant difference, this means:
   A) There is not enough evidence to conclude a difference
   B) The groups are identical
   C) The null hypothesis is true
   D) There is no true difference

Answer 16

A

Question 17

16. A higher standard deviation affects power by:
    A) Narrowing the confidence interval
    B) Decreasing power
    C) Reducing required sample size
    D) Increasing power

Answer 17

B

Question 18

17. What does it mean if p = 0.5 in a hypothesis test?
    A) A large effect exists
    B) The null hypothesis is likely false
    C) There’s a 50% chance the null hypothesis is true
    D) The data are very consistent with the null hypothesis

Answer 18

D

Question 19

18. In a simulation study comparing 50 implants and 50 endodontic cases, a 5% difference is found. Why might this result not be statistically significant?
    A) The sample size is too large
    B) The data are skewed
    C) The difference is too large to test
    D) There’s too little power due to small sample size

Answer 19

D

Question 20

19. A trial detects a statistically significant result (p < 0.05). What else must you consider before claiming clinical relevance?
    A) Effect size and clinical context
    B) Power
    C) Confidence level
    D) Type I error

Answer 20

A

Question 21

20. In a two-tailed test, what does a p-value of 0.003 imply?
    A) The result lies in one tail
    B) The alternative hypothesis is false
    C) The test is one-sided
    D) There’s a 0.3% chance of observing data this extreme under H₀

Answer 21

D

Question 22

A) Reducing the effect size
B) Using a smaller sample
C) Increasing the standard deviation
D) Raising the significance level (α) from 0.01 to 0.05

Answer 22

D

Question 23

22. In hypothesis testing, a Type II error means:
    A) You incorrectly reject the null hypothesis
    B) You correctly reject the alternative hypothesis
    C) You fail to reject the null hypothesis when the alternative is true
    D) You observe a p-value less than 0.05

Answer 23

C

Question 24

23. What is the main role of standard error in hypothesis testing?
    A) It determines the sample mean
    B) It measures sampling variability
    C) It gives the maximum value of the dataset
    D) It determines Type I error rate

Answer 24

B

Question 25

24. Which of the following study designs is most likely to result in low power? A) A study with a large effect size and 1000 participants B) A study with a small sample and high measurement variability C) A study with a narrow confidence interval D) A study with low standard deviation and 95% confidence

Answer 25

B

Question 26

25. You plan a study to detect a mean difference in blood pressure between two treatments. Which input do you not need for the sample size calculation? A) Desired significance level B) Expected effect size C) Power D) The sample mean

Answer 26

D

Question 27

26. If you observe a difference between groups, but the p-value is greater than 0.05, what should you conclude? A) There is no difference B) The result is statistically significant C) The difference is real D) There is not enough evidence to reject the null hypothesis

Answer 27

D

Question 28

27. What is the most likely reason that a small study finds a non-significant result, even if there’s a real difference? A) Type I error B) Low standard deviation C) High power D) Insufficient power

Answer 28

D

Question 29

28. A researcher reports a p-value of 0.049. Which statement is most accurate? A) There is overwhelming evidence of a large effect B) The result is not statistically significant C) There is evidence to reject the null hypothesis at the 5% level D) This proves the alternative hypothesis is true

Answer 29

C

Question 30

29. As sample size increases, which of the following occurs (assuming all else constant)? A) The standard error increases B) The p-value increases C) The standard error decreases D) Type I error increases

Answer 30

C

Question 31

30. You conduct a study with 90% power and a significance level of 0.05. What does the 10% represent? A) Probability of a Type I error B) Confidence interval width C) Probability of a Type II error D) The effect size

Answer 31

c

Question 32

31. A p-value of 0.045 suggests: A) The result is statistically significant at the 5% level B) There is a 4.5% chance that the alternative hypothesis is true C) The null hypothesis should definitely be accepted D) The result is not significant

Answer 32

a

Question 33

32. Which of the following best defines the null hypothesis (H₀)? A) There is a true effect in the population B) There is no association between the variables of interest C) The alternative explanation is correct D) A clinically important effect is present

Answer 33

b

Question 34

33. A Type I error is: A) Failing to reject a false null hypothesis B) Concluding there is no effect when one exists C) Rejecting a true null hypothesis D) Having insufficient power

Answer 34

C

Question 35

34. You’re calculating the sample size for a new clinical trial. Which factor decreases the required sample size? A) Choosing a lower significance level B) Using a smaller effect size C) Reducing variability in the outcome D) Targeting lower power (e.g. 60%) instead of 80%

Answer 35

C

Question 36

35. If a hypothesis test gives p = 0.51, which is the most appropriate conclusion? A) The result is statistically significant B) We can reject the null hypothesis C) The evidence does not support a difference at α = 0.05 D) The null hypothesis is false

Answer 36

C

Question 37

36. What is the primary advantage of increasing your study’s sample size? A) It guarantees significance B) It reduces the mean C) It increases power D) It increases the p-value

Answer 37

C

Question 38

37. What does a statistically significant result (p < 0.05) actually tell you? A) The alternative hypothesis is certainly true B) The null hypothesis is unlikely given the data C) The result is large and clinically meaningful D) The sample was too small

Answer 38

B

Question 39

38. Which of the following would increase the risk of a Type II error? A) A large effect size B) A very small sample size C) A high standard deviation D) Both B and C

Answer 39

D

Question 40

39. In hypothesis testing, which statement is true about power? A) Power = 1 – Type I error B) Power is always 0.05 C) Power is the probability of detecting a true effect D) Power is determined only by the effect size

Answer 40

C

Question 41

40. Which statement is false about statistical significance? A) A statistically significant result means an effect exists B) Statistical significance proves causation C) Statistical significance can occur even with a small effect D) Large sample sizes can make tiny effects statistically significant

Answer 41

B