MCQ Quiz: Applying Biostatistics for Study Appraisal

A solid understanding of biostatistics is indispensable for PharmD students to critically appraise biomedical literature and apply evidence-based medicine in clinical practice. The ability to interpret study results, understand statistical significance, and recognize potential biases is crucial for making informed decisions about patient care. This quiz focuses on key biostatistical concepts essential for evaluating published research, including levels of measurement, hypothesis testing, measures of central tendency and dispersion, p-values, confidence intervals, and the interpretation of common statistical tests encountered in medical literature.

1. Which of the following describes nominal data?

• a) Data that can be ranked or ordered, but the differences between ranks are not necessarily equal.
• b) Data where categories have no natural order or ranking.
• c) Data measured on a scale with equal intervals between points, but no true zero.
• d) Data measured on a scale with equal intervals and a true zero point.

Answer: b) Data where categories have no natural order or ranking.

2. Temperature measured in Celsius or Fahrenheit is an example of what type of data?

• a) Nominal
• b) Ordinal
• c) Interval
• d) Ratio

Answer: c) Interval

3. The measure of central tendency that represents the most frequently occurring value in a dataset is the:

• a) Mean
• b) Median
• c) Mode
• d) Range

Answer: c) Mode

4. Which measure of central tendency is most affected by extreme outliers in a dataset?

• a) Mean
• b) Median
• c) Mode
• d) Interquartile range

Answer: a) Mean

5. The median is defined as:

• a) The arithmetic average of all values.
• b) The middle value in a dataset that has been ordered from least to greatest.
• c) The value that occurs with the highest frequency.
• d) The difference between the highest and lowest values.

Answer: b) The middle value in a dataset that has been ordered from least to greatest.

6. Which of the following is a measure of dispersion or variability in a dataset?

• a) Mean
• b) Median
• c) Mode
• d) Standard Deviation

Answer: d) Standard Deviation

7. A p-value is best described as:

• a) The probability that the null hypothesis is true.
• b) The probability of observing the study results, or more extreme results, if the null hypothesis is true.
• c) The probability that the alternative hypothesis is true.
• d) The probability of making a Type II error.

Answer: b) The probability of observing the study results, or more extreme results, if the null hypothesis is true.

8. In hypothesis testing, what does the null hypothesis typically state?

• a) There is a significant difference between groups.
• b) There is no difference or no association between the variables being studied.
• c) The study intervention is effective.
• d) The observed effect is due to chance.

Answer: b) There is no difference or no association between the variables being studied.

9. A Type I error (alpha error) in hypothesis testing occurs when:

• a) The null hypothesis is incorrectly accepted when it is false.
• b) The null hypothesis is incorrectly rejected when it is true. [cite: 725]
• c) The alternative hypothesis is incorrectly accepted when it is false.
• d) The alternative hypothesis is incorrectly rejected when it is true.

Answer: b) The null hypothesis is incorrectly rejected when it is true.

10. A Type II error (beta error) in hypothesis testing occurs when:

• a) The null hypothesis is incorrectly rejected when it is true.
• b) The null hypothesis is incorrectly accepted when it is false.
• c) The study has too much power.
• d) The p-value is very small.

Answer: b) The null hypothesis is incorrectly accepted when it is false.

11. The power of a study is defined as:

• a) The probability of making a Type I error.
• b) The probability of correctly rejecting a false null hypothesis.
• c) The probability of making a Type II error.
• d) The probability that the null hypothesis is true.

Answer: b) The probability of correctly rejecting a false null hypothesis.

12. A 95% confidence interval for a mean difference is calculated as -2.5 to 0.5. What is the correct interpretation?

• a) There is a 95% probability that the true mean difference falls between -2.5 and 0.5.
• b) We are 95% confident that the true mean difference in the population lies between -2.5 and 0.5.
• c) 95% of the sample data falls between -2.5 and 0.5.
• d) The result is always statistically significant because the interval is narrow.

Answer: b) We are 95% confident that the true mean difference in the population lies between -2.5 and 0.5.

13. If a 95% confidence interval for an odds ratio includes the value 1.0, what can be concluded about statistical significance at the 0.05 alpha level?

• a) The result is statistically significant.
• b) The result is not statistically significant.
• c) The study has high power.
• d) The study has low power.

Answer: b) The result is not statistically significant.

14. Which statistical test is most appropriate for comparing the means of two independent groups with normally distributed continuous data?

• a) Chi-square test
• b) Paired t-test
• c) Independent samples t-test
• d) ANOVA

Answer: c) Independent samples t-test

15. Which statistical test is used to compare the means of three or more independent groups with normally distributed continuous data?

• a) Independent samples t-test
• b) Paired t-test
• c) ANOVA (Analysis of Variance)
• d) Chi-square test

Answer: c) ANOVA (Analysis of Variance)

16. The chi-square test is typically used to analyze what type of data?

• a) Continuous data from two independent groups.
• b) Categorical (nominal or ordinal) data to assess for associations between variables.
• c) Continuous data from three or more groups.
• d) Paired continuous data.

Answer: b) Categorical (nominal or ordinal) data to assess for associations between variables.

17. What does “correlation” measure?

• a) The causal relationship between two variables.
• b) The strength and direction of the linear relationship between two continuous variables.
• c) The difference in means between two groups.
• d) The proportion of variance in one variable explained by another.

Answer: b) The strength and direction of the linear relationship between two continuous variables.

18. A correlation coefficient (r) of -0.8 indicates:

• a) A weak positive linear relationship.
• b) A strong positive linear relationship.
• c) A weak negative linear relationship.
• d) A strong negative linear relationship.

Answer: d) A strong negative linear relationship.

19. What is the primary purpose of regression analysis?

• a) To compare means between multiple groups.
• b) To assess the association between categorical variables.
• c) To model the relationship between a dependent variable and one or more independent variables.
• d) To determine the frequency of an event.

Answer: c) To model the relationship between a dependent variable and one or more independent variables.

20. Incidence is defined as:

• a) The total number of existing cases of a disease at a specific point in time.
• b) The number of new cases of a disease occurring in a population at risk over a specified period.
• c) The proportion of a population that has a disease at a specific point in time.
• d) The rate at which individuals recover from a disease.

Answer: b) The number of new cases of a disease occurring in a population at risk over a specified period.

21. Prevalence is defined as:

• a) The number of new cases of a disease over a period.
• b) The proportion of a population that has a particular disease at a specific point in time or over a period.
• c) The rate at which new cases develop.
• d) The risk of developing a disease.

Answer: b) The proportion of a population that has a particular disease at a specific point in time or over a period.

22. Relative Risk (RR) is commonly used in which type of study design?

• a) Case-control studies
• b) Cohort studies
• c) Cross-sectional studies
• d) Case reports

Answer: b) Cohort studies

23. An RR of 2.0 means that the exposed group has:

• a) Half the risk of the outcome compared to the unexposed group.
• b) The same risk of the outcome as the unexposed group.
• c) Twice the risk of the outcome compared to the unexposed group.
• d) A 2% increased risk of the outcome.

Answer: c) Twice the risk of the outcome compared to the unexposed group.

24. Odds Ratio (OR) is commonly calculated in which type of study design?

• a) Randomized controlled trials
• b) Cohort studies
• c) Case-control studies
• d) Cross-sectional studies

Answer: c) Case-control studies

25. Absolute Risk Reduction (ARR) is calculated as:

• a) The risk in the exposed group divided by the risk in the unexposed group.
• b) The difference in risk of an outcome between the control group and the treatment group.
• c) The inverse of the Number Needed to Treat (NNT).
• d) The risk in the treatment group minus the risk in the control group.

Answer: b) The difference in risk of an outcome between the control group and the treatment group.

26. Number Needed to Treat (NNT) is defined as:

• a) The number of patients who need to be treated for one patient to experience an adverse event.
• b) The number of patients who need to be treated for one patient to benefit from the treatment.
• c) The percentage of patients who benefit from treatment.
• d) The absolute risk reduction multiplied by 100.

Answer: b) The number of patients who need to be treated for one patient to benefit from the treatment.

27. Number Needed to Harm (NNH) is defined as:

• a) The number of patients who need to be treated for one patient to benefit.
• b) The number of patients who need to be exposed to a risk factor for one to develop the disease.
• c) The number of patients who need to receive an intervention for one patient to experience an adverse event.
• d) The relative risk of harm.

Answer: c) The number of patients who need to receive an intervention for one patient to experience an adverse event.

28. Confounding occurs when:

• a) The study results are not statistically significant.
• b) The effect of an exposure on an outcome is distorted by the presence of another variable associated with both the exposure and the outcome.
• c) There is a strong correlation between two variables.
• d) The sample size is too small.

Answer: b) The effect of an exposure on an outcome is distorted by the presence of another variable associated with both the exposure and the outcome.

29. Randomization in a clinical trial primarily helps to:

• a) Ensure that patients know which treatment they are receiving.
• b) Increase the external validity of the study.
• c) Minimize selection bias and balance known and unknown confounding factors between treatment groups.
• d) Guarantee statistical significance.

Answer: c) Minimize selection bias and balance known and unknown confounding factors between treatment groups.

30. Intention-to-Treat (ITT) analysis in a clinical trial means that:

• a) Only patients who completed the treatment protocol are included in the analysis.
• b) Patients are analyzed in the group to which they were originally assigned, regardless of whether they received or completed the treatment.
• c) Patients who did not adhere to treatment are excluded.
• d) Only patients who experienced the primary outcome are analyzed.

Answer: b) Patients are analyzed in the group to which they were originally assigned, regardless of whether they received or completed the treatment.

31. A forest plot is commonly used in which type of study?

• a) Case reports
• b) Cross-sectional studies
• c) Meta-analyses
• d) Qualitative studies

Answer: c) Meta-analyses

32. Heterogeneity in a meta-analysis refers to:

• a) The similarity of results across studies.
• b) The variability in results across studies beyond what would be expected by chance.
• c) The consistency of the study populations.
• d) The use of the same outcome measures in all studies.

Answer: b) The variability in results across studies beyond what would be expected by chance.

33. Publication bias in a meta-analysis is the tendency for:

• a) Studies with positive or statistically significant results to be more likely to be published.
• b) Studies with negative results to be published more frequently.
• c) Larger studies to be published more often.
• d) Only high-quality studies to be published.

Answer: a) Studies with positive or statistically significant results to be more likely to be published.

34. Sensitivity of a diagnostic test is defined as:

• a) The probability that a person without the disease will test negative.
• b) The probability that a person with the disease will test positive.
• c) The probability that a person with a positive test actually has the disease.
• d) The probability that a person with a negative test actually does not have the disease.

Answer: b) The probability that a person with the disease will test positive.

35. Specificity of a diagnostic test is defined as:

• a) The probability that a person with the disease will test positive.
• b) The probability that a person without the disease will test negative.
• c) The probability that a person with a positive test actually has the disease.
• d) The probability that a person with a negative test actually does not have the disease.

Answer: b) The probability that a person without the disease will test negative.

36. Positive Predictive Value (PPV) of a diagnostic test is:

• a) The probability that a person with the disease will test positive.
• b) The probability that a person without the disease will test negative.
• c) The probability that a person who tests positive actually has the disease.
• d) The probability that a person who tests negative actually does not have the disease.

Answer: c) The probability that a person who tests positive actually has the disease.

37. Negative Predictive Value (NPV) of a diagnostic test is:

• a) The probability that a person with the disease will test positive.
• b) The probability that a person without the disease will test negative.
• c) The probability that a person who tests positive actually has the disease.
• d) The probability that a person who tests negative actually does not have the disease.

Answer: d) The probability that a person who tests negative actually does not have the disease.

38. A Receiver Operating Characteristic (ROC) curve plots:

• a) Sensitivity versus (1 – Specificity) for various cut-off points of a diagnostic test.
• b) Prevalence versus incidence.
• c) P-values versus sample size.
• d) Relative risk versus odds ratio.

Answer: a) Sensitivity versus (1 – Specificity) for various cut-off points of a diagnostic test.

39. The area under the ROC curve (AUC) is a measure of:

• a) The prevalence of the disease.
• b) The overall accuracy or discriminatory power of a diagnostic test.
• c) The incidence of the disease.
• d) The statistical significance of the test.

Answer: b) The overall accuracy or discriminatory power of a diagnostic test.

40. Which of the following is an example of ordinal data?

• a) Blood type (A, B, AB, O)
• b) Pain scale (e.g., mild, moderate, severe)
• c) Body temperature in degrees Celsius
• d) Patient’s age in years

Answer: b) Pain scale (e.g., mild, moderate, severe)

41. If a researcher sets the alpha level at 0.01 instead of 0.05, what is the impact on Type I error?

• a) The probability of a Type I error increases.
• b) The probability of a Type I error decreases.
• c) The probability of a Type I error remains unchanged.
• d) The probability of a Type II error decreases.

Answer: b) The probability of a Type I error decreases.

42. Statistical significance (e.g., p < 0.05) implies that:

• a) The observed result is clinically important.
• b) The observed result is unlikely to be due to chance alone.
• c) The null hypothesis is definitely false.
• d) The study has no flaws.

Answer: b) The observed result is unlikely to be due to chance alone.

43. Which of the following graphical displays is most appropriate for showing the distribution of a single continuous variable?

• a) Bar chart
• b) Pie chart
• c) Histogram
• d) Scatter plot

Answer: c) Histogram

44. A Kaplan-Meier curve is used to display:

• a) The correlation between two variables.
• b) The distribution of a continuous variable.
• c) Survival probabilities over time.
• d) The results of a diagnostic test.

Answer: c) Survival probabilities over time.

45. The interquartile range (IQR) is a measure of statistical dispersion, representing:

• a) The difference between the maximum and minimum values.
• b) The range containing the middle 50% of the data (Q3 – Q1).
• c) The average deviation from the mean.
• d) The most frequent value.

Answer: b) The range containing the middle 50% of the data (Q3 – Q1).

46. Parametric statistical tests, like the t-test and ANOVA, generally assume that the data are:

• a) Categorical.
• b) Skewed.
• c) Normally distributed.
• d) Ordinal.

Answer: c) Normally distributed.

47. Non-parametric tests are often used when:

• a) The sample size is very large.
• b) The data are normally distributed.
• c) The assumptions for parametric tests are not met (e.g., data are not normally distributed).
• d) The data are interval or ratio level.

Answer: c) The assumptions for parametric tests are not met (e.g., data are not normally distributed).

48. What is a “confidence level” in the context of a confidence interval?

• a) The probability that the calculated interval contains the true population parameter.
• b) The width of the confidence interval.
• c) The p-value associated with the interval.
• d) The sample size used to calculate the interval.

Answer: a) The probability that the calculated interval contains the true population parameter.

49. If a study reports a result as “statistically non-significant,” it means that:

• a) The intervention has no effect.
• b) The study failed to demonstrate a statistically significant effect, which could be due to insufficient power or a true lack of effect.
• c) The p-value was greater than 0.5.
• d) The confidence interval was very narrow.

Answer: b) The study failed to demonstrate a statistically significant effect, which could be due to insufficient power or a true lack of effect.

50. The critical appraisal of a research paper involves evaluating its:

• a) Length and number of authors.
• b) Validity, importance, and applicability of the findings.
• c) Journal impact factor only.
• d) Use of complex statistical methods.

Answer: b) Validity, importance, and applicability of the findings.