Biostatistics Practice Questions

Q: Which statistical test is appropriate for comparing ten blood pressure readings taken before and after treatment?

Paired t-test. **Explanation:** The correct answer is **A. Paired t-test**. In this scenario, the data consists of quantitative (numerical) measurements—blood pressure—taken from the **same group of individuals** at two different points in time (before and after treatment). This is a classic example of **"paired" or "dependent" data**, where each "before" value has a direct match with an "after" value. The Paired t-test is specifically designed to compare the means of two related groups to determine if the intervention (treatment) caused a statistically significant change. **Why other options are incorrect:** * **Z-test:** Used for comparing means when the sample size is large (typically **n > 30**) and the population variance is known. Here, the sample size is small (n=10). * **Student’s t-test (Unpaired/Independent):** Used to compare the means of two **independent** groups (e.g., comparing BP between Group A and Group B). It does not account for the relationship between "before and after" readings in the same person. * **Correlation test:** Measures the strength and direction of a linear relationship between two variables (e.g., height and weight), but it does not compare means or determine the effectiveness of a treatment. **High-Yield Clinical Pearls for NEET-PG:** * **Quantitative Data + 2 Groups (Dependent/Matched):** Paired t-test. * **Quantitative Data + 2 Groups (Independent):** Unpaired t-test. * **Quantitative Data + >2 Groups:** ANOVA (Analysis of Variance). * **Qualitative (Categorical) Data:** Chi-square test. * If the data is not normally distributed (Non-parametric), the alternative to the Paired t-test is the **Wilcoxon Signed Rank Test**.

Q: Which of the following is NOT a method of survival analysis?

Survival rate. ### Explanation The core of this question lies in distinguishing between a **statistical method (test)** and a **descriptive measure (parameter)**. **Why "Survival Rate" is the correct answer:** Survival rate is a **descriptive statistic** (a proportion or percentage) that indicates the fraction of people in a study group who are alive after a certain period (e.g., 5-year survival rate). It is an **outcome measure**, not a mathematical method or test used to analyze time-to-event data. **Analysis of Incorrect Options:** * **A. Kaplan-Meier Method:** This is the most common **non-parametric** method used to estimate the survival function. It uses "product-limit" calculations and is ideal for small samples where the exact time of death/event is known for each subject. * **B. Actuarial Method (Life Table):** Also known as the "Interval-based" method, it is used for large samples. It calculates survival probabilities over fixed time intervals (e.g., 1 year, 5 years) rather than at the exact time of each event. * **C. Kruskal-Wallis Test:** While this is a non-parametric test, it is used to compare the medians of **three or more independent groups**. It is essentially the non-parametric alternative to one-way ANOVA. Since it can be used to compare survival times across multiple groups, it is considered a tool within the broader scope of survival analysis. **High-Yield NEET-PG Pearls:** * **Survival Analysis:** Used when the outcome of interest is the **time** until an event occurs (Time-to-event data). * **Censoring:** A unique feature of survival analysis where the event has not occurred for a subject by the end of the study or they are lost to follow-up. * **Log-Rank Test:** The most common statistical test used to compare the survival curves of two or more groups. * **Cox Proportional Hazards Model:** A semi-parametric regression method used to investigate the relationship between survival time and several predictor variables.

Q: Which of the following study designs is BEST for establishing a causal association in the etiology of a disease?

Randomized controlled trial. ### Explanation The strength of a study design in establishing causality is determined by its position on the **Hierarchy of Evidence**. **Why Randomized Controlled Trial (RCT) is the Correct Answer:** An RCT is considered the "Gold Standard" for establishing causal associations. Its superiority lies in **Randomization**, which ensures that both known and unknown confounding factors are distributed equally between the study and control groups. By eliminating selection bias and controlling the exposure (intervention), any difference in outcome can be confidently attributed to the intervention itself, fulfilling the criteria for **temporality** and **biological plausibility**. **Analysis of Incorrect Options:** * **Cohort Study:** While it is the best *observational* study for establishing temporality (exposure precedes outcome) and calculating incidence, it is prone to selection bias and confounding, making it less definitive than an RCT. * **Case-control Study:** This is a retrospective design. It is useful for rare diseases and generating hypotheses but is highly susceptible to **recall bias** and cannot prove a direct cause-and-effect relationship. * **Ecological Study:** This uses populations or groups as the unit of study rather than individuals. It is prone to **Ecological Fallacy**, where associations observed at the group level may not apply to individuals. **NEET-PG High-Yield Pearls:** 1. **Hierarchy of Evidence (Descending order):** Meta-analysis/Systemic Reviews > RCT > Cohort > Case-Control > Case Series > Case Report. 2. **Temporality:** The only Bradford Hill criteria for causality that is absolutely essential. 3. **Randomization:** Known as the "Heart of an RCT," it eliminates **Selection Bias**. 4. **Blinding:** Primarily used to eliminate **Observer/Measurement Bias**.

Q: When the dependent or outcome variable is survival data, what regression method is applied?

Cox regression analysis. ### Explanation **Correct Answer: C. Cox regression analysis** **Why it is correct:** Cox regression (or **Cox Proportional Hazards Model**) is the gold standard for analyzing **survival data**. Survival data is unique because it involves "time-to-event" outcomes (e.g., time until death, relapse, or recovery). Unlike other regressions, it can handle **censored data**—cases where the event hasn't occurred by the end of the study or the patient is lost to follow-up. It calculates the **Hazard Ratio (HR)**, which estimates the risk of the event occurring at any given point in time based on various independent variables. **Why the other options are incorrect:** * **A. Multiple Linear Regression:** This is used when the dependent variable is **continuous and numerical** (e.g., predicting blood pressure or BMI). It cannot account for time-to-event or censoring. * **B. Multiple Logistic Regression:** This is used when the dependent variable is **dichotomous/binary** (e.g., Yes/No, Dead/Alive). While it predicts the probability of an outcome, it ignores *when* the outcome occurred. **High-Yield Clinical Pearls for NEET-PG:** * **Kaplan-Meier Curve:** A non-parametric method used to *estimate and visualize* survival probabilities over time. It does not account for multiple covariates (unlike Cox regression). * **Log-Rank Test:** The statistical test used to *compare* the survival curves of two or more groups. * **Hazard Ratio (HR):** If HR = 1 (No difference); HR > 1 (Increased risk/harm); HR < 1 (Protective effect). * **Key Distinction:** Use **Logistic Regression** for "if" an event happens; use **Cox Regression** for "when" an event happens.

Q: If the annual growth rate of a population is 1.2%, in how many years is the population likely to get doubled?

47-50 years. ### Explanation **1. Why the Correct Answer is Right** The time required for a population to double in size is calculated using the **Rule of 70**. This is a simplified mathematical formula used in demography and biostatistics to estimate doubling time based on a constant annual growth rate. The formula is: **Doubling Time (T) = 70 / Annual Growth Rate (r)** Applying the values from the question: * Growth Rate (r) = 1.2% * Doubling Time = 70 / 1.2 = **58.33 years** Looking at the options provided, **Option D (47-50 years)** is the closest approximation to the calculated value. In many standard textbooks (like Park’s Preventive and Social Medicine), the "Rule of 70" or "Rule of 69" is used to explain that a 1% growth rate doubles a population in 70 years, while a 2% rate doubles it in 35 years. At 1.2%, the value falls between 50 and 60 years. **2. Why Other Options are Wrong** * **Option A (18-20 years):** This would require a growth rate of approximately 3.5% to 3.8% ($70/18 \approx 3.8$). * **Option B (20-23 years):** This would require a growth rate of approximately 3% ($70/23 \approx 3$). * **Option C (28-35 years):** This corresponds to a growth rate of 2% to 2.5% ($70/2 \approx 35$). **3. Clinical Pearls & High-Yield Facts for NEET-PG** * **Rule of 70 vs. 69:** While 70 is easier for mental calculation, some statisticians use 69.3 (natural log of 2) for higher precision. * **India’s Context:** According to the 2011 Census, India's annual exponential growth rate was approximately 1.64%. * **Demographic Gap:** The difference between the Crude Birth Rate (CBR) and Crude Death Rate (CDR) determines the natural increase in population. * **Vital Index:** (Births / Deaths) × 100. It measures the population's biological success.

Q: Statistical power of a trial is equal to:

1 - beta (Type II error rate). ### Explanation **1. Understanding the Correct Answer (B):** The **Power of a Study** is the probability that a test will correctly reject a null hypothesis when it is false (i.e., the ability of a study to detect a true difference or effect). * **Type II Error ($\beta$):** Occurs when we fail to reject a null hypothesis that is actually false (a "false negative"). * **Power ($1 - \beta$):** Represents the probability of avoiding a Type II error. If $\beta$ is 0.20 (20%), the power is 0.80 (80%), meaning there is an 80% chance of detecting a statistically significant difference if one truly exists. **2. Analysis of Incorrect Options:** * **Option A ($1 + \alpha$):** This is a mathematically invalid expression in biostatistics. * **Option C ($\alpha + \beta$):** This represents the sum of the probabilities of making a Type I and Type II error, which does not define any specific statistical parameter. * **Option D ($\alpha / \beta$):** This ratio is not used to define power. However, the relationship between $\alpha$ and $\beta$ is inverse; decreasing the risk of a Type I error typically increases the risk of a Type II error for a fixed sample size. **3. NEET-PG High-Yield Pearls:** * **Type I Error ($\alpha$):** "False Positive" – Rejecting the null hypothesis when it is true. (Fixed by the p-value, usually 0.05). * **Type II Error ($\beta$):** "False Negative" – Accepting the null hypothesis when it is false. * **Determinants of Power:** Power increases with **increased sample size**, increased effect size, and increased $\alpha$ level. * **Standard Values:** In most clinical trials, the minimum acceptable power is **80%**. * **Confidence Level:** Defined as **$1 - \alpha$** (the probability of correctly accepting the null hypothesis when it is true).

Q: Which of the following is NOT a distinct feature of a case-control study method?

The unit of study is a group rather than an individual.. **Explanation** In epidemiology, a **Case-Control Study** is an observational, analytical study used to determine the association between an exposure and an outcome. **Why Option B is the correct answer:** The unit of study in a case-control study is the **individual**. Researchers identify individuals with the disease (cases) and individuals without the disease (controls) to compare their past exposures. If the unit of study were a **group or population**, it would be classified as an **Ecological Study**. This is a common "trap" question in NEET-PG regarding levels of organization in study designs. **Analysis of other options:** * **Option A:** This is a hallmark of case-control studies. They are **retrospective**; both the exposure and the outcome have already occurred by the time the investigator begins the study. * **Option C:** Case-control studies proceed from **"Effect to Cause."** You start with the effect (the disease) and look backward in time to find the cause (the risk factor/exposure). * **Option D:** The presence of a **comparison group** (controls) is what makes this an "analytical" study rather than a "descriptive" one. Controls provide the baseline frequency of exposure to support or refute the statistical association. **High-Yield Clinical Pearls for NEET-PG:** * **Measure of Association:** Case-control studies use the **Odds Ratio (OR)**. They cannot calculate Incidence or Relative Risk (RR). * **Best for:** Rare diseases or diseases with long latent periods. * **Main Bias:** **Recall Bias** is the most common disadvantage, as cases may remember past exposures more vividly than controls. * **Starting Point:** Always starts with the **Disease**.

Question 1

Which statistical test is appropriate for comparing ten blood pressure readings taken before and after treatment?

Accepted Answer

Paired t-test

Answer

Z-test

Answer

Student's t-test

Answer

Correlation test

Question 2

Which statement is true regarding the hierarchy of study designs?

Accepted Answer

A systematic review or meta-analysis is at the top of the study design pyramid.

Answer

A systematic review is the same as a narrative review.

Answer

A randomized controlled trial is a better design than a meta-analysis and systematic review.

Answer

A systematic review is not applicable for case-control studies.

Question 3

Which of the following is NOT a method of survival analysis?

Accepted Answer

Survival rate

Answer

Kaplan-Meier method

Answer

Actuarial method

Answer

Kruskal-Wallis test

Question 4

Which of the following study designs is BEST for establishing a causal association in the etiology of a disease?

Accepted Answer

Randomized controlled trial

Answer

Case-control study

Answer

Cohort study

Answer

Ecological study

Question 5

When the dependent or outcome variable is survival data, what regression method is applied?

Accepted Answer

Cox regression analysis

Answer

Multiple linear regression analysis

Answer

Multiple logistic regression analysis

Answer

None of the above

Question 6

Select the true statement regarding correlation.

Accepted Answer

In moderately positive correlation, a rise in one variable leads to a rise in the other.

Answer

In perfectly negative correlation, a rise in one variable leads to a proportional rise in the other.

Answer

In moderately negative correlation, a fall in one variable leads to a proportional fall in the other.

Answer

In perfectly positive correlation, a rise in one variable leads to a proportional fall in the other.

Question 7

If the annual growth rate of a population is 1.2%, in how many years is the population likely to get doubled?

Accepted Answer

47-50 years

Answer

18-20 years

Answer

20-23 years

Answer

28-35 years

Question 8

Statistical power of a trial is equal to:

Accepted Answer

1 - beta (Type II error rate)

Answer

1 + alpha (Type I error rate)

Answer

alpha + beta

Answer

alpha / beta

Question 9

Which of the following is NOT a distinct feature of a case-control study method?

Accepted Answer

The unit of study is a group rather than an individual.

Answer

Both exposure and outcome have occurred before the start of the study.

Answer

The study proceeds backward from effect to cause.

Answer

It uses a control or comparison group to support or refute an inference.

Question 10

A population pyramid is an example of which type of graphical representation?

Accepted Answer

Bar chart

Answer

Histogram

Answer

Frequency polygon

Answer

Pie chart

Biostatistics — MCQs

Biostatistics — MCQs

On this page

Practice by Chapter

Want unlimited practice?