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Biostatistics & Epidemiology Biostatistics — Step 1 practice questions
188 questions in this area, with an explanation for every answer choice.
Three samples below, free and without an account.
How this shows up on Step 1
Biostatistics questions in this section combine two things the exam tests separately elsewhere. The most self-contained discipline on the exam, and the most reliably scorable. Reliably the most learnable section on the exam.
Sample questions
Sample question 1
A study with a small sample size reports a very wide 95% confidence interval for its effect estimate. Which of the following best explains this finding?
Explanations
A. Sample size has no relationship to confidence interval width
Sample size has a well-established, direct relationship to confidence interval width — larger samples generally produce narrower, more precise intervals.
B. Wide confidence intervals always indicate a fraudulent study
A wide confidence interval reflects statistical uncertainty due to sample size/variability, not necessarily fraud.
C. Confidence interval width is determined solely by the significance level and nothing else
While significance level (e.g., 95% vs 99%) affects interval width, sample size and variability are also major determining factors, not just significance level alone.
D. A wide confidence interval means the study found no effect at all, definitively
A wide confidence interval reflects imprecision, not necessarily "no effect" — the interval could still exclude the null value despite being wide, or could span both meaningful and null effects, requiring careful interpretation.
E. Smaller sample sizes generally produce less precise (wider) confidence intervals due to greater sampling variability · correct
Smaller sample sizes are associated with greater sampling variability, producing wider (less precise) confidence intervals around the point estimate, reflecting greater uncertainty about the true population value.
Sample question 2
A screening program reduces disease-specific mortality from 0.5% to 0.3% over 10 years. Approximately how many people must be screened for 10 years to prevent one death from this disease?
Explanations
A. 500 · correct
Absolute risk reduction = 0.5% − 0.3% = 0.2% = 0.002. Number needed to screen = 1 / ARR = 1 / 0.002 = 500. Expressing screening benefit this way is more informative than the relative risk reduction (40% here), which sounds much more impressive while describing the same small absolute benefit — an important framing consideration in shared decision-making.
B. 2,000
2,000 would correspond to an ARR of 0.05%.
C. 50
50 would require an ARR of 2%.
D. 5,000
5,000 corresponds to an ARR of 0.02%.
E. 200
200 would correspond to an ARR of 0.5%.
Sample question 3
Researchers enroll a cohort of smokers and non-smokers and follow them forward for 20 years to determine the incidence of lung cancer in each group. Which measure of association can this study calculate that a case-control study cannot?
Explanations
A. Correlation coefficient
A correlation coefficient measures a linear relationship between two continuous variables, not the standard risk measure for this type of exposure-outcome study.
B. Neither can calculate any risk measure
Cohort studies absolutely can and do calculate risk measures, including relative risk.
C. Odds ratio
Odds ratios can be calculated by BOTH cohort and case-control studies; this is not the distinguishing measure.
D. Relative risk · correct
A cohort study, by following defined groups forward and observing actual disease incidence, can calculate relative risk directly — something a case-control study (which starts with a fixed number of cases/controls) cannot do.
E. Prevalence ratio only
While prevalence can be measured in some cross-sectional contexts, incidence-based relative risk is the key distinguishing measure for a cohort study here.
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