Biostatistics Practice Questions

Q: Find the value of the range for the following data: 7, 9, 6, 8, 11, 10, 4?

7. ### Explanation **1. Why Option C is Correct:** In biostatistics, the **Range** is the simplest measure of dispersion. It represents the numerical difference between the highest (maximum) and the lowest (minimum) values in a given data set. * **Formula:** Range = Maximum Value – Minimum Value * **Data Set:** 7, 9, 6, 8, 11, 10, 4 * **Maximum Value:** 11 * **Minimum Value:** 4 * **Calculation:** 11 – 4 = **7** **2. Why Other Options are Incorrect:** * **Option A (5):** This might be obtained if one incorrectly identifies the minimum value as 6 instead of 4 (11 – 6 = 5). * **Option B (6):** This might be obtained by subtracting the first value from the last value (7 – 4 = 3) or other calculation errors; it does not represent the spread between the extremes. * **Option D (8):** This might occur if the maximum value is misidentified or if there is a calculation error (e.g., 12 – 4). **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **Simplest Measure:** Range is the easiest measure of dispersion to calculate but is the most unstable because it depends only on two extreme values. * **Sensitivity to Outliers:** The range is highly influenced by extreme values (outliers). If one patient in a study has an unusually high blood pressure reading, the range will increase significantly, even if the rest of the group is stable. * **Interquartile Range (IQR):** To overcome the limitation of outliers, the IQR is used. It measures the distance between the 75th percentile ($Q_3$) and the 25th percentile ($Q_1$) and is the preferred measure of dispersion for **skewed data**. * **Standard Deviation:** This is the most commonly used and most important measure of dispersion in medical research as it accounts for every value in the distribution.

Q: Which of the following is NOT true about the chi-square test?

It directly measures the strength of association.. The **Chi-square ($\chi^2$) test** is a non-parametric test used to analyze categorical (nominal) data. It is primarily a test of **significance**, not a measure of magnitude. ### Why Option C is the Correct Answer (The "NOT" True Statement) The Chi-square test determines whether an association between two variables is likely due to chance (p-value). However, it **does not directly measure the strength or intensity of that association**. To measure the strength of association in categorical data, one must use other indices like **Relative Risk (RR)**, **Odds Ratio (OR)**, or **Cramer’s V**. A very small p-value from a Chi-square test indicates high statistical significance, but it does not necessarily mean the clinical association is "strong." ### Analysis of Other Options * **Option A:** It is the standard test for comparing two or more **proportions** (e.g., comparing the recovery rate of Drug A vs. Drug B). * **Option B:** Its primary purpose is to test the **null hypothesis**, confirming if an association exists between two qualitative variables. * **Option C:** It is versatile and can compare multiple groups (e.g., 2x2, 2x3, or 3x3 contingency tables), unlike the Z-test which is limited to two groups. ### High-Yield Clinical Pearls for NEET-PG * **Yates’ Correction:** Applied to a 2x2 table when any expected cell frequency is **less than 5**. * **Fisher’s Exact Test:** Used instead of Chi-square when the sample size is very small (total $N < 40$ or any expected cell frequency is **less than 2**). * **Degrees of Freedom (df):** Calculated as $(r-1) \times (c-1)$. For a standard 2x2 table, $df = 1$. * **Type of Data:** Always used for **Qualitative/Categorical** data. For Quantitative data, use Student’s t-test or ANOVA.

Q: All of the following statements regarding case-control and cohort studies are true, EXCEPT:

Cohort studies are suitable to investigate rare diseases.. ### Explanation The correct answer is **C**, as cohort studies are **not** suitable for investigating rare diseases. #### 1. Why Option C is the Correct Answer (The Exception) In a **cohort study**, researchers follow a group of people over time to see who develops a disease. If a disease is rare (e.g., a specific rare cancer), a researcher would need to follow an impossibly large number of people for a very long time to observe even a few cases. Therefore, **Case-control studies** are the design of choice for rare diseases, as they start with people who already have the disease. #### 2. Analysis of Other Options * **Option A:** In case-control studies, subjects are selected based on their **outcome/disease status** (Cases vs. Controls), and then researchers look backward to assess their **history of exposure**. This is a fundamental characteristic of the design. * **Option B:** Cohort studies are usually **prospective**; they follow subjects from exposure to the development of the disease, which naturally requires a significant amount of time (often years). Case-control studies are retrospective and much faster. * **Option C:** A single cohort (e.g., smokers) can be monitored for the development of multiple outcomes, such as lung cancer, COPD, and coronary artery disease. #### 3. High-Yield NEET-PG Pearls * **Rare Disease:** Use Case-Control Study. * **Rare Exposure:** Use Cohort Study (e.g., workers in a specific chemical factory). * **Incidence:** Can only be calculated directly from a **Cohort Study**. * **Odds Ratio (OR):** The measure of association for Case-Control. * **Relative Risk (RR) and Attributable Risk (AR):** The measures of association for Cohort Studies. * **Recall Bias:** A major disadvantage of Case-Control studies.

Q: Which of the following is NOT true about the Sample Registration System?

It is the same as the census.. **Explanation:** The **Sample Registration System (SRS)** is a large-scale demographic survey in India designed to provide reliable annual estimates of vital statistics. **1. Why Option A is the Correct Answer (The "NOT True" statement):** The SRS is **not** the same as the Census. The **Census** is a decennial (every 10 years) exercise that covers the entire population (complete enumeration). In contrast, the **SRS is a sample-based survey** that covers only a representative part of the population to estimate vital rates for the periods between two censuses. **2. Analysis of Other Options:** * **Option B (Dual Record System):** This is true. The SRS uses two independent methods to collect data: continuous enumeration by a resident part-time enumerator and an independent retrospective half-yearly survey by a full-time supervisor. * **Option C (Reliable estimates):** This is true. Because the Civil Registration System (CRS) in India has historically faced under-reporting, the SRS serves as the primary and most reliable source for Birth Rate, Death Rate, and Infant Mortality Rate (IMR) at the national and state levels. * **Option D (Retrospective half-yearly system):** This is true. As part of the dual-record system, a supervisor conducts an independent check every six months to record events that occurred during the previous half-year. **High-Yield NEET-PG Pearls:** * **Origin:** SRS was initiated on a pilot basis in 1964-65 and became fully operational in 1969-70. * **Authority:** It is conducted by the **Office of the Registrar General of India (RGI)**, Ministry of Home Affairs. * **Key Utility:** It is the "Gold Standard" for measuring **IMR (Infant Mortality Rate)** and **MMR (Maternal Mortality Ratio)** in India. * **Comparison:** While Census provides data on population size/distribution, SRS provides data on population dynamics (fertility and mortality).

Q: For a Randomized Control Trial (RCT) to assess dating in adolescents, a study was conducted by selecting random schools, then random classes, then random sections, and finally random students. This is an example of:

Multistage sampling. ### Explanation **Correct Answer: D. Multistage Sampling** **Why it is correct:** Multistage sampling is a complex form of probability sampling where the sample is selected in several stages using smaller and smaller sampling units at each stage. In this scenario, the researcher follows a hierarchical progression: **Schools (1st stage) → Classes (2nd stage) → Sections (3rd stage) → Students (Final stage)**. Unlike cluster sampling, where all elements within a selected group are studied, multistage sampling involves further random selection at every level until the final individual unit is reached. **Why the other options are incorrect:** * **A. Stratified Sampling:** This involves dividing a heterogeneous population into homogeneous groups (strata) based on a specific characteristic (e.g., age, gender) and then taking a random sample from *each* stratum. Here, the selection is based on hierarchy, not specific population traits. * **B. Simple Random Sampling:** This is the "lottery method" where every individual in the entire population has an equal chance of being selected. It is impractical for large, geographically dispersed populations like all school students. * **C. Cluster Sampling:** In this method, the population is divided into groups (clusters), a few clusters are randomly selected, and *everyone* within those selected clusters is studied. If the researcher had studied every student in the selected sections, it would be cluster sampling. **High-Yield Clinical Pearls for NEET-PG:** * **Multistage Sampling** is the most common method used in large-scale national health surveys (e.g., NFHS in India). * **Cluster Sampling** is the method of choice for the **WHO Expanded Programme on Immunization (EPI)** coverage surveys (30 clusters × 7 children). * **Precision:** Simple Random Sampling usually has the highest precision, while Multistage/Cluster sampling has lower precision but higher feasibility for field research.

Q: A woman with exposure to multiple sexual partners has a 5 times increased risk for carcinoma of the cervix. What is the attributable risk?

80%. ### Explanation **1. Understanding the Correct Answer (C: 80%)** The question provides the **Relative Risk (RR)**, which is 5. It asks for the **Attributable Risk (AR)**, specifically the **Attributable Risk Percent (AR%)**, also known as the Etiologic Fraction. This measure indicates the proportion of the disease in the exposed group that can be directly attributed to the risk factor. The formula for Attributable Risk Percent is: $$AR\% = \frac{RR - 1}{RR} \times 100$$ Plugging in the values: $$AR\% = \frac{5 - 1}{5} \times 100 = \frac{4}{5} \times 100 = 80\%$$ This means that 80% of cervical cancer cases among women with multiple sexual partners can be attributed to that specific exposure, and if the exposure were eliminated, the incidence in this group would drop by 80%. **2. Why Other Options are Incorrect** * **A (20%):** This represents the reciprocal of the risk (1/5), which has no standard epidemiological meaning in this context. * **B (50%):** This would be the result if the RR was 2. * **D (100%):** This would only occur if the exposure was the *sole* cause of the disease (RR = infinity), which is rarely the case in multifactorial diseases. **3. NEET-PG High-Yield Pearls** * **Relative Risk (RR):** Measures the *strength* of association (used in Cohort studies). * **Attributable Risk (AR):** Measures the *impact* of a risk factor on the exposed group (useful for clinical prevention). * **Population Attributable Risk (PAR):** Measures the impact of a risk factor on the *entire population* (useful for public health policy). * **Carcinoma Cervix:** The primary risk factor is HPV (Types 16, 18). Multiple sexual partners increase the risk of HPV transmission.

Q: All of the following statistical tests are used to analyze variables with normal distribution except?

Chi square test. **Explanation** The core concept tested here is the distinction between **Parametric** and **Non-parametric** tests. * **Parametric tests** (Options A, C, and D) assume that the data follows a **Normal (Gaussian) Distribution** and are used for quantitative (numerical) data. * **Non-parametric tests** (Option B) make no assumptions about the distribution of the data (distribution-free) and are primarily used for qualitative (categorical) data. **Why Chi-square test is the correct answer:** The Chi-square test is a non-parametric test used to compare proportions and associations between **categorical/nominal variables** (e.g., gender, smoking status). Since it does not require the data to follow a normal distribution, it is the "except" in this list. **Analysis of incorrect options:** * **Student’s t-test:** A parametric test used to compare the means of two groups. It requires the data to be normally distributed. * **ANOVA (Analysis of Variance):** An extension of the t-test used to compare the means of three or more groups. It also assumes a normal distribution. * **Multiple Linear Regression:** A parametric method used to model the relationship between one dependent variable and multiple independent variables. It assumes that the residuals (errors) are normally distributed. **High-Yield Clinical Pearls for NEET-PG:** 1. **Quantitative Data + 2 groups:** Use Student’s t-test (Unpaired for independent groups, Paired for before-after studies). 2. **Quantitative Data + >2 groups:** Use ANOVA. 3. **Qualitative Data:** Use Chi-square test (or Fisher’s Exact test if the sample size is very small/cell frequency <5). 4. **Non-parametric alternatives:** If data is not normally distributed, use **Mann-Whitney U test** (instead of unpaired t-test) or **Kruskal-Wallis test** (instead of ANOVA).

Q: In a test of significance, if the P value is 0.023, what can be concluded about the observed difference in the study?

The null hypothesis is rejected, and the study is accepted.. ### Explanation **1. Understanding the Correct Answer (Option B)** In biostatistics, the **P-value** represents the probability that the observed difference occurred by chance. By convention, the threshold for statistical significance (alpha level) is set at **0.05**. * If **P < 0.05**: The result is "statistically significant." We **reject the Null Hypothesis ($H_0$)** (which claims there is no difference) and **accept the Alternative Hypothesis ($H_1$)**. * In this case, $P = 0.023$ (which is $ 0.05$ (e.g., $P = 0.23$), indicating the results are likely due to chance. * **Option D:** This is logically inconsistent. If you reject the null hypothesis, you are essentially validating that the study has found a significant effect/difference, so the study results are "accepted" as statistically valid. **3. High-Yield Clinical Pearls for NEET-PG** * **P-value vs. Alpha:** $P$ is the calculated probability from the data; $\alpha$ (usually 0.05) is the pre-determined cutoff. * **Type I Error ($\alpha$):** Rejecting a null hypothesis that is actually true (False Positive). The P-value is the probability of committing a Type I error. * **Type II Error ($\beta$):** Failing to reject a null hypothesis that is actually false (False Negative). * **Power of Study ($1-\beta$):** The ability of a study to detect a difference if one truly exists. * **Significant vs. Highly Significant:** $P < 0.05$ is significant; $P < 0.01$ is often termed "highly significant."

Q: In a community, 20% of the population is below 15 years of age and 15% is above 65 years of age. Calculate the dependency ratio.

54%. ### Explanation **1. Understanding the Correct Answer (C: 54%)** The **Dependency Ratio** is a demographic measure used to understand the economic burden on the productive portion of a population. It is defined as the ratio of the "dependent" population (those not typically in the labor force) to the "working-age" population. * **Formula:** $$\text{Dependency Ratio} = \frac{(\text{Population } 65 \text{ years})}{\text{Population between 15–64 years}} \times 100$$ * **Calculation for this question:** * Young dependents ( 65 years) = 15% * Total dependents = 20 + 15 = 35% * Working-age population = 100% – (Total dependents) = 100 – 35 = 65% * **Dependency Ratio** = $(35 / 65) \times 100 = \mathbf{53.84\%}$ (rounded to **54%**). **2. Why Other Options are Incorrect** * **Option A (34%) & B (40%):** These are mathematical miscalculations or represent only one segment of the dependency (e.g., just the young or old) without dividing by the working-age denominator. * **Option D (85%):** This likely results from incorrectly using the total population (100) as the denominator or misidentifying the working-age group. **3. NEET-PG High-Yield Pearls** * **Total Dependency Ratio:** Sum of young and old dependency. * **Young Age Dependency Ratio:** $(\text{Pop } 65 / \text{Pop } 15\text{–}64) \times 100$. * **Demographic Dividend:** Occurs when the dependency ratio declines due to a bulge in the working-age population (15–64 years), leading to potential economic growth. * **Note:** In some Indian contexts, the working age is occasionally cited as 15–59 years; however, for standard international biostatistics and most NEET-PG questions, **15–64 years** is the gold standard denominator.

Q: In a village population, individuals are arranged alphabetically, and then every 8th person is selected for the study. What type of study design is this?

Systematic random sampling. ### Explanation **1. Why Systematic Random Sampling is Correct:** Systematic random sampling is a probability sampling method where the sample is chosen based on a fixed, periodic interval (the **sampling interval, 'k'**). * **The Process:** First, a list of the population is created (the sampling frame). Then, a starting point is chosen at random, and every $k^{th}$ individual is selected thereafter. * **In this question:** The alphabetical arrangement provides the sampling frame, and selecting every **8th person** represents the sampling interval ($k=8$). This "regular interval" approach is the hallmark of systematic sampling. **2. Why the Other Options are Incorrect:** * **Simple Random Sampling:** Every individual has an equal and independent chance of being selected (e.g., lottery method or random number table). It does not follow a fixed numerical pattern like "every 8th person." * **Stratified Random Sampling:** The population is first divided into homogenous subgroups (strata) based on characteristics like age, sex, or income, and then samples are drawn from each stratum. No such grouping occurred here. * **Cluster Sampling:** Used when the population is large and spread out. The population is divided into "clusters" (e.g., city blocks or villages), and entire clusters are selected at random. Here, individuals are being selected, not groups. **3. High-Yield Clinical Pearls for NEET-PG:** * **Sampling Interval ($k$):** Calculated as $N/n$ (Total Population / Sample Size). * **Advantage:** It is simpler and more convenient than simple random sampling and ensures even spread across the list. * **Potential Bias:** If the list has a hidden periodic pattern that coincides with the sampling interval (periodicity), the sample may not be representative. * **Comparison:** Systematic sampling is often called **"Quasi-random"** because only the first unit is selected truly at random.

Question 1

Find the value of the range for the following data: 7, 9, 6, 8, 11, 10, 4?

Accepted Answer

7

Answer

5

Answer

6

Answer

8

Question 2

Which of the following is NOT true about the chi-square test?

Accepted Answer

It directly measures the strength of association.

Answer

It tests the significance of the difference between two proportions.

Answer

It tells about the presence or absence of an association between two variables.

Answer

It can be used when more than two groups are to be compared.

Question 3

All of the following statements regarding case-control and cohort studies are true, EXCEPT:

Accepted Answer

Cohort studies are suitable to investigate rare diseases.

Answer

Case-control studies are chosen based on the history of exposure.

Answer

Cohort studies require a longer time frame than case-control studies.

Answer

Cohort studies yield information about more than one disease.

Question 4

Which of the following is NOT true about the Sample Registration System?

Accepted Answer

It is the same as the census.

Answer

It is a dual record system.

Answer

It provides a reliable estimate of birth rate and death rate.

Answer

It is an independent, retrospective, half-yearly system.

Question 5

For a Randomized Control Trial (RCT) to assess dating in adolescents, a study was conducted by selecting random schools, then random classes, then random sections, and finally random students. This is an example of:

Accepted Answer

Multistage sampling

Answer

Stratified sampling

Answer

Simple random sampling

Answer

Cluster sampling

Question 6

A woman with exposure to multiple sexual partners has a 5 times increased risk for carcinoma of the cervix. What is the attributable risk?

Accepted Answer

80%

Answer

20%

Answer

50%

Answer

100%

Question 7

All of the following statistical tests are used to analyze variables with normal distribution except?

Accepted Answer

Chi square test

Answer

Student t-test

Answer

ANOVA

Answer

Multiple linear regression

Question 8

In a test of significance, if the P value is 0.023, what can be concluded about the observed difference in the study?

Accepted Answer

The null hypothesis is rejected, and the study is accepted.

Answer

The null hypothesis is accepted, and the study is rejected.

Answer

The null hypothesis is accepted, and the study is accepted.

Answer

The null hypothesis is rejected, and the study is also rejected.

Question 9

In a community, 20% of the population is below 15 years of age and 15% is above 65 years of age. Calculate the dependency ratio.

Accepted Answer

54%

Answer

34%

Answer

40%

Answer

85%

Question 10

In a village population, individuals are arranged alphabetically, and then every 8th person is selected for the study. What type of study design is this?

Accepted Answer

Systematic random sampling

Answer

Simple random sampling

Answer

Stratified random sampling

Answer

Cluster sampling

Biostatistics — MCQs

Biostatistics — MCQs

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