Biostatistics Practice Questions

Q: What is the definition of statistical power?

The ability to reject a false null hypothesis.. **Statistical Power** is a fundamental concept in clinical research that measures a study’s ability to detect an effect (or difference) when one truly exists. ### Why Option B is Correct Statistical Power (represented as **1 – β**) is the probability that a test will correctly **reject a false null hypothesis**. In clinical terms, if a new drug is truly effective (the null hypothesis is false), power is the likelihood that the study will yield a statistically significant result confirming that effectiveness. A power of 0.80 (80%) is generally considered the minimum acceptable level for clinical trials. ### Explanation of Incorrect Options * **Option A:** Rejecting a *true* null hypothesis is a **Type I Error (α)**. This is a "false positive," where you claim a difference exists when it actually does not. * **Option C:** Correctly accepting (or failing to reject) a *true* null hypothesis is known as **Confidence Level (1 – α)**. * **Option D:** Accepting (failing to reject) a *false* null hypothesis is a **Type II Error (β)**. This is a "false negative," where the study fails to detect a real effect. ### NEET-PG High-Yield Pearls * **The Relationship:** Power is inversely related to Type II error (**Power = 1 – β**). As the risk of a false negative decreases, the power of the study increases. * **Factors Increasing Power:** 1. **Increased Sample Size (N):** The most common way to boost power. 2. **Increased Effect Size:** Larger differences are easier to detect. 3. **Decreased Standard Deviation:** Less "noise" in the data makes the "signal" clearer. 4. **Increased Alpha (α):** Though rarely done, increasing the significance level (e.g., from 0.05 to 0.10) increases power.

Q: Observing alcohol intake over 20 years and its association with the occurrence of liver disease is an example of what type of study?

Cohort study. ### Explanation **Why Cohort Study is Correct:** A **Cohort study** is a longitudinal, observational study that starts with a group of individuals (the cohort) who are currently free of the disease but differ in their exposure to a potential risk factor. In this scenario, the researchers start with people based on their **exposure** (alcohol intake) and follow them forward in time (**prospective**) for 20 years to observe the **outcome** (liver disease). The key feature here is the direction of inquiry: **Exposure $\rightarrow$ Outcome**. This allows for the calculation of Incidence and Relative Risk (RR). **Why Other Options are Incorrect:** * **B. Case-control study:** This study starts with the outcome (people who already have liver disease) and looks backward in time (**retrospective**) to find exposures. The direction is **Outcome $\rightarrow$ Exposure**. * **C. Ecological study:** This uses **populations or groups** as the unit of study (e.g., comparing alcohol sales in different countries to liver cirrhosis rates) rather than tracking specific individuals over time. * **D. Randomized Controlled Trial (RCT):** This is an **experimental** study where the investigator intervenes (e.g., giving a drug). It would be unethical to intentionally assign individuals to consume alcohol to see if they develop liver disease. **NEET-PG High-Yield Pearls:** * **Incidence:** Can only be calculated from Cohort studies. * **Best for Rare Exposures:** Cohort Study. * **Best for Rare Diseases:** Case-control Study. * **Temporal Association:** Cohort studies are the best observational design to establish that the exposure preceded the disease (Temporality is the strongest Hill’s Criterion). * **Attrition Bias:** The biggest challenge in long-term (20-year) cohort studies is "loss to follow-up."

Q: If the coefficient of correlation between height and weight is 2.6, what is true?

The calculation of the coefficient is wrong. ### Explanation **1. Why the Correct Answer is Right:** The **Pearson Correlation Coefficient (r)** is a statistical measure that quantifies the strength and direction of a linear relationship between two continuous variables. A fundamental mathematical property of the correlation coefficient is that its value **must always lie between -1 and +1** ($-1 \leq r \leq +1$). * **r = +1:** Perfect positive correlation. * **r = -1:** Perfect negative correlation. * **r = 0:** No linear correlation. In this question, the value provided is **2.6**, which exceeds the maximum possible limit of +1. Therefore, such a value is mathematically impossible, indicating that the calculation is incorrect. **2. Why the Other Options are Wrong:** * **Option A (Positive correlation):** While a positive number usually indicates a positive correlation, the value must be $\leq 1$. Since 2.6 is invalid, we cannot conclude the nature of the correlation. * **Option B (No association):** No association is represented by $r = 0$. * **Option C (Negative correlation):** A negative correlation is represented by a value between 0 and -1. **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **Coefficient of Determination ($r^2$):** This is the square of the correlation coefficient. it represents the proportion of variance in one variable that is predictable from the other. (e.g., if $r = 0.6$, then $r^2 = 0.36$ or 36%). * **Independence of Units:** The value of 'r' is independent of the units of measurement (e.g., whether height is in cm or inches, 'r' remains the same). * **Scatter Diagram:** The best visual method to represent correlation. * **Regression vs. Correlation:** Correlation measures the *strength* of association, while Regression measures the *nature* of the relationship to predict the value of a dependent variable.

Q: Contraceptive efficacy is measured by?

Pearl index and Life table analysis. ### Explanation **1. Why the Correct Answer is Right:** Contraceptive efficacy refers to the ability of a method to prevent pregnancy under ideal or typical conditions. It is measured using two primary statistical tools: * **Pearl Index:** This is the most common method. It calculates the number of accidental pregnancies per 100 woman-years of exposure. * *Formula:* (Total accidental pregnancies × 1200) / (Total months of exposure). * **Life Table Analysis:** This is considered more accurate than the Pearl Index. It calculates the "failure rate" at specific intervals (e.g., at 6 months, 12 months). It accounts for "drop-outs" (people who stop using the method for reasons other than pregnancy), providing a more cumulative and realistic risk assessment over time. **2. Why Other Options are Wrong:** * **Option A (Pearl Index only):** While widely used, it is not the *only* measure. It fails to account for the fact that failure rates often decrease the longer a couple uses a method (the "selection effect"). * **Options C & D (Couple Protection Rate):** The **Couple Protection Rate (CPR)** is a management indicator used to monitor the performance of Family Welfare Programmes. It measures the percentage of eligible couples effectively protected against childbirth by various methods. It is a measure of **program coverage**, not the biological efficacy of the contraceptive itself. **3. High-Yield Facts for NEET-PG:** * **Pearl Index of Common Methods:** * No Method: 80–90 * OCPs (Typical use): 9 * Copper T 380A: 0.8 * Vasectomy: 0.1 (Most effective) * **Net Reproduction Rate (NRR):** The goal of the National Health Policy is to achieve NRR = 1 (replacement level fertility). * **Eligible Couple:** A currently married couple where the wife is in the reproductive age group (15–49 years).

Q: What is the study design of choice for determining the diurnal variation of fat content in expressed breast milk for preterm babies?

Cross-sectional study. ### Explanation **Why Cross-sectional Study is Correct:** A cross-sectional study (also known as a **prevalence study**) is designed to examine the relationship between variables at a **single point in time** or over a short, defined interval. In this scenario, the objective is to measure the fat content in breast milk at specific times of the day (diurnal variation). Since the researchers are "taking a snapshot" of the milk composition at various points (e.g., morning vs. evening) without following the subjects over a long period to see an outcome, it fits the definition of a cross-sectional design. It is the most efficient way to determine the **prevalence or status** of a physiological parameter at a specific moment. **Analysis of Incorrect Options:** * **Prospective Cohort Study:** This involves following a group of exposed and unexposed individuals over time to see who develops a disease. It is used to determine **incidence and relative risk**, which is not the goal here. * **Ambispective Cohort Study:** This combines retrospective (past records) and prospective (future follow-up) data. It is unnecessarily complex for measuring a physiological baseline like milk fat. * **Case-Control Study:** This is a retrospective study that starts with an outcome (disease) and looks back for exposures. Since there is no "disease" or "control" group being compared for risk factors, this design is inapplicable. **High-Yield Pearls for NEET-PG:** * **Snapshot Rule:** If the study measures "What is happening right now?" it is Cross-sectional. * **Sequence:** Cross-sectional studies are often the first step in exploring a hypothesis before committing to a longitudinal cohort. * **Breast Milk Fact:** Fat is the most variable macronutrient in breast milk; it typically increases as the breast is emptied (hindmilk > foremilk) and often shows a diurnal peak in the evening.

Q: A population in Hardy-Weinberg equilibrium has individuals expressing a rare autosomal recessive disease. The frequency of affected individuals in this population is 1 in 90,000. What is the frequency of carriers in this population?

1 in 150. ### Explanation This question tests the application of the **Hardy-Weinberg Principle**, which states that allele and genotype frequencies in a population remain constant from generation to generation in the absence of evolutionary influences. **1. Why Option B is Correct:** The Hardy-Weinberg equation is: **$p^2 + 2pq + q^2 = 1$**, where: * $q^2$ = Frequency of affected individuals (homozygous recessive) * $2pq$ = Frequency of carriers (heterozygotes) * $p$ = Frequency of the dominant allele **Step-by-step Calculation:** * **Given:** $q^2 = 1/90,000$ * **Find $q$:** $q = \sqrt{1/90,000} = 1/300$ * **Find $p$:** Since $p + q = 1$, and $q$ is very small (rare disease), $p \approx 1$. * **Find Carrier Frequency ($2pq$):** $2 \times 1 \times (1/300) = 2/300 = \mathbf{1/150}$. **2. Why Other Options are Incorrect:** * **Option A (1 in 100):** This would be the carrier frequency if the disease prevalence ($q^2$) was 1 in 40,000. * **Option C (1 in 200):** This would be the carrier frequency if the disease prevalence ($q^2$) was 1 in 160,000. * **Option D (1 in 250):** This would be the carrier frequency if the disease prevalence ($q^2$) was 1 in 250,000. **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **The Shortcut:** For rare autosomal recessive diseases, the carrier frequency is approximately **$2 \times \sqrt{\text{Prevalence}}$**. * **Hardy-Weinberg Assumptions:** The population must be large, have random mating, and no mutation, selection, or migration. * **Application:** This principle is used in genetic counseling to estimate the risk of a couple having an affected child when only the population prevalence is known. * **Recessive vs. Dominant:** If a disease is rare, the number of carriers ($2pq$) is always significantly higher than the number of affected individuals ($q^2$).

Q: All of the following show the relationship between two variables except?

Line diagram. ### Explanation In biostatistics, the choice of a diagram or statistical test depends on whether we are analyzing a single variable (univariate) or the relationship between two variables (bivariate). **1. Why "Line Diagram" is the Correct Answer:** A **Line Diagram** (or Line Chart) is primarily used to show the **trend of a single variable over a period of time** (e.g., maternal mortality rates over the last decade). It plots the value of a variable on the Y-axis against time on the X-axis. Since it tracks the progression of one characteristic, it does not demonstrate a correlation or relationship between two independent variables. **2. Analysis of Incorrect Options:** * **Scatter Diagram:** This is the most common graphic method to visualize the relationship between two quantitative variables. Each dot represents a pair of values $(x, y)$. The pattern of dots indicates the direction and strength of the relationship. * **Dot Diagram (Dot Plot):** While often used for small datasets to show distribution, in the context of bivariate data, a dot plot can represent the relationship between a categorical variable and a continuous variable, or act as a simplified scatter plot. * **Correlation Coefficient ($r$):** This is a mathematical measure (ranging from -1 to +1) that quantifies the degree and direction of the linear relationship between two quantitative variables. **High-Yield Clinical Pearls for NEET-PG:** * **Scatter Diagram:** Only shows the *existence* of a relationship; it does not prove *causation*. * **Correlation ($r$):** Measures the strength of association. $r = +1$ is perfect positive correlation; $r = -1$ is perfect negative correlation. * **Regression:** Used to *predict* the value of one variable based on the other. * **Histogram/Bar Chart:** Used for frequency distributions, not for showing relationships between two different variables.

Q: What was the sex ratio of children aged 0-6 years according to the 2011 census?

914. **Explanation:** The **Child Sex Ratio (CSR)** is defined as the number of females per 1,000 males in the age group of 0–6 years. According to the **2011 Census of India**, the Child Sex Ratio was recorded as **914**, showing a declining trend from 927 in the 2001 Census. This decline is a critical public health and demographic concern, often attributed to the preference for male children and the misuse of diagnostic technologies for sex-selective abortion. **Analysis of Options:** * **Option B (914):** This is the correct figure for the Child Sex Ratio (0–6 years) as per the 2011 Census. * **Option A (940):** This represents the **Overall Sex Ratio** (total females per 1,000 males) in India according to the 2011 Census. * **Option C (944):** This was the Child Sex Ratio recorded during the **1991 Census**. * **Option D (933):** This was the **Overall Sex Ratio** recorded during the **2001 Census**. **High-Yield Facts for NEET-PG:** * **Highest Child Sex Ratio (State):** Arunachal Pradesh (972). * **Lowest Child Sex Ratio (State):** Haryana (834). * **Highest Overall Sex Ratio (State):** Kerala (1084). * **Lowest Overall Sex Ratio (UT):** Daman & Diu (618). * **PNDT Act (1994):** The Pre-Conception and Pre-Natal Diagnostic Techniques Act was enacted to stop female feticide and address the declining CSR. * **Formula:** $CSR = \frac{\text{Number of girls (0–6 years)}}{\text{Number of boys (0–6 years)}} \times 1000$.

Question 1

What is the definition of statistical power?

Accepted Answer

The ability to reject a false null hypothesis.

Answer

The ability to reject a true null hypothesis.

Answer

The ability to accept a true null hypothesis.

Answer

The ability to accept a false null hypothesis.

Question 2

Observing alcohol intake over 20 years and its association with the occurrence of liver disease is an example of what type of study?

Accepted Answer

Cohort study

Answer

Case control study

Answer

Ecological study

Answer

Randomized controlled trial (RCT)

Question 3

If the coefficient of correlation between height and weight is 2.6, what is true?

Accepted Answer

The calculation of the coefficient is wrong

Answer

Positive correlation

Answer

No association

Answer

Negative correlation

Question 4

Contraceptive efficacy is measured by?

Accepted Answer

Pearl index and Life table analysis

Answer

Pearl index only

Answer

Life table analysis and Couple protection rate

Answer

Pearl index and Couple protection rate

Question 5

In a certain population, there were 4050 births in the last one year. There were 50 stillbirths, 50 infants died within 7 days, and 150 died within 28 days. What is the neonatal mortality rate?

Accepted Answer

50

Answer

62.5

Answer

12.5

Answer

49.4

Question 6

What is the study design of choice for determining the diurnal variation of fat content in expressed breast milk for preterm babies?

Accepted Answer

Cross-sectional study

Answer

Prospective cohort study

Answer

Ambispective cohort study

Answer

Case-control study

Question 7

A population in Hardy-Weinberg equilibrium has individuals expressing a rare autosomal recessive disease. The frequency of affected individuals in this population is 1 in 90,000. What is the frequency of carriers in this population?

Accepted Answer

1 in 150

Answer

1 in 100

Answer

1 in 200

Answer

1 in 250

Question 8

The Z score criteria is applicable to which of the following distributions?

Accepted Answer

Normal distribution

Answer

Chi-square test

Answer

Skewed distribution

Answer

Paired t-test

Question 9

All of the following show the relationship between two variables except?

Accepted Answer

Line diagram

Answer

Correlation coefficient

Answer

Scatter diagram

Answer

Dot diagram

Question 10

What was the sex ratio of children aged 0-6 years according to the 2011 census?

Accepted Answer

914

Answer

940

Answer

944

Answer

933

Biostatistics — MCQs

Biostatistics — MCQs

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