Biostatistics Practice Questions

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.

Q: Out of 11 births in a hospital, 5 babies weighed over 2.5 kg and 5 weighed less than 2.5 kg. What value does 2.5 represent?

Median. ### Explanation **Correct Answer: C. Median** The **Median** is defined as the middle-most value in a distribution when the data points are arranged in ascending or descending order. It divides the distribution into two equal halves, such that 50% of the observations lie above it and 50% lie below it. In this scenario: * Total births ($n$) = 11. * Observations above 2.5 kg = 5. * Observations below 2.5 kg = 5. * The 2.5 kg value occupies the 6th position (the exact center), making it the median. --- ### Why other options are incorrect: * **Arithmetic Average (Mean):** This is the sum of all observations divided by the total number of observations. We cannot calculate the mean here because the specific weights of the other 10 babies are not provided. * **Geometric Average:** This is the $n^{th}$ root of the product of all observations. It is typically used for rates and ratios (e.g., bacterial growth or population growth) and is not applicable here. * **Mode:** This represents the most frequently occurring value in a dataset. The question does not state that 2.5 kg is the most common weight, only that it is the central point. --- ### High-Yield Clinical Pearls for NEET-PG: 1. **Best Measure of Central Tendency:** * For **Normally distributed (symmetrical) data**: Mean is preferred. * For **Skewed data (outliers)**: Median is the most robust measure as it is not affected by extreme values. 2. **Relationship in Skewed Data:** * **Right (Positive) Skew:** Mean > Median > Mode. * **Left (Negative) Skew:** Mode > Median > Mean. 3. **Median Calculation:** For an odd number of observations, the median is the $(\frac{n+1}{2})^{th}$ value. For an even number, it is the average of the two middle values.

Q: If the mean is 209, the median is 196, and the mode is 135, what type of distribution does this indicate?

Positively skewed. ### Explanation **1. Why Positively Skewed is Correct:** In a frequency distribution, the relationship between the three measures of central tendency (Mean, Median, and Mode) determines the "skewness" or asymmetry of the curve. * **The Rule:** In a **Positively Skewed** distribution (also known as Right-skewed), the tail of the curve extends toward the higher values (right side). * **The Relationship:** **Mean > Median > Mode**. * **In this question:** Mean (209) > Median (196) > Mode (135). Since the mean is pulled toward the higher extreme values, it confirms a positive skew. **2. Why Incorrect Options are Wrong:** * **Standard Curve (Normal Distribution):** In a perfectly symmetrical bell-shaped curve, the **Mean = Median = Mode**. Here, the values are significantly different. * **Negatively Skewed:** In a left-skewed distribution, the tail extends toward the lower values. The relationship is reversed: **Mean Median). * **Sensitivity to Outliers:** The **Mean** is the most affected by extreme values (outliers), while the **Mode** is the least affected. * **Karl Pearson’s Formula:** For moderately skewed distributions: * $Mode = (3 \times Median) - (2 \times Mean)$ * **Clinical Example:** Income distribution or incubation periods of most infectious diseases (like Salmonellosis) typically show a positive skew.

Q: What type of data description is represented by a stem-and-leaf diagram?

Stem and leaf diagram. ### Explanation **Correct Answer: A. Stem and leaf diagram** **Understanding the Concept:** A **Stem-and-Leaf diagram** is a unique hybrid tool in biostatistics that acts as both a **tabular and a graphical representation** of data. It is used to describe the **frequency distribution** of a quantitative dataset. * The "Stem" represents the leading digit(s) (e.g., tens), and the "Leaf" represents the trailing digit (e.g., units). * Unlike a histogram, it **retains the individual raw data values** while simultaneously showing the shape of the distribution (skewness, outliers, and modal class). This makes it an excellent tool for small to medium-sized datasets where seeing every data point is necessary. **Why the other options are incorrect:** * **B. Box Whisker Plot:** This is used to represent the **five-number summary** of a dataset (Minimum, First Quartile, Median, Third Quartile, and Maximum). It is primarily used to visualize dispersion and identify outliers, but it does not show individual raw data points like a stem-and-leaf plot. * **C. Forrest Plot:** This is a graphical display used specifically in **Meta-analysis**. It illustrates the individual results (odds ratios/relative risks) of multiple studies and provides a "pooled" or summary effect size. * **D. Funnel Plot:** This is a scatter plot used in Meta-analysis to detect **Publication Bias**. A symmetrical funnel indicates no bias, while an asymmetrical funnel suggests the presence of bias. **NEET-PG High-Yield Pearls:** * **Stem-and-Leaf vs. Histogram:** Both show distribution shape, but only the Stem-and-Leaf plot preserves the original data values. * **Quantitative Data Tools:** Histograms, Frequency Polygons, and Box plots are for quantitative data. * **Qualitative Data Tools:** Bar charts, Pie charts, and Pictograms are for qualitative data. * **Scatter Diagram:** Used to show the **correlation** between two continuous variables.

Q: If neonatal deaths are 450, stillbirths are 212, and total live births are 12450, what is the Neonatal Mortality Rate (NMR)?

36. **Explanation:** The **Neonatal Mortality Rate (NMR)** is a key indicator of newborn care and maternal health. It is defined as the number of deaths of live-born infants during the first 28 completed days of life per 1,000 live births in a given year. **Calculation:** * **Formula:** (Number of neonatal deaths / Total live births) × 1000 * **Data provided:** Neonatal deaths = 450; Live births = 12,450. * **Calculation:** (450 / 12,450) × 1000 = **36.14 per 1000 live births.** * Rounding to the nearest whole number gives **36**. **Analysis of Options:** * **Option B (36):** Correct. This follows the standard formula using only live births in the denominator. * **Option A (17):** Incorrect. This value does not correlate with the provided data. * **Option C (64):** Incorrect. This value is approximately the **Perinatal Mortality Rate (PMR)**. PMR includes (Stillbirths + Early Neonatal Deaths) / (Live births + Stillbirths) × 1000. If one mistakenly adds stillbirths to the numerator and total births to the denominator, they arrive at a higher figure (~52), but 64 is mathematically incorrect for NMR. * **Option D (92):** Incorrect. This is a distractor value significantly higher than typical NMR ranges. **High-Yield Clinical Pearls for NEET-PG:** * **Denominator Rule:** For NMR, IMR, and U5MR, the denominator is always **Live Births**. For Perinatal Mortality Rate and Maternal Mortality Rate (Ratio), the denominator includes **Total Births** (Live births + Stillbirths). * **Early Neonatal Death:** Death within 0–7 days of birth. * **Late Neonatal Death:** Death between 7–28 days of birth. * **Most common cause of Neonatal Mortality in India:** Prematurity and low birth weight (followed by birth asphyxia and sepsis).

Question 1

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 2

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 3

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 4

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 5

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

Question 6

The American Diabetes Association (ADA) recently lowered the cut-off value for fasting glucose used in diagnosing diabetes mellitus from 140 mg/dL to 126 mg/dL. This reference interval change would be expected to produce which of the following alterations?

Accepted Answer

Increase the test's negative predictive value

Answer

Decrease the test's sensitivity

Answer

Increase the test's false negative rate

Answer

Increase the test's positive predictive value

Question 7

Out of 11 births in a hospital, 5 babies weighed over 2.5 kg and 5 weighed less than 2.5 kg. What value does 2.5 represent?

Accepted Answer

Median

Answer

Geometric average

Answer

Arithmetic average

Answer

Mode

Question 8

If the mean is 209, the median is 196, and the mode is 135, what type of distribution does this indicate?

Accepted Answer

Positively skewed

Answer

Standard curve

Answer

Negatively skewed

Answer

J-shaped

Question 9

What type of data description is represented by a stem-and-leaf diagram?

Accepted Answer

Stem and leaf diagram

Answer

Box whisker plot

Answer

Forrest plot

Answer

Funnel plot

Question 10

If neonatal deaths are 450, stillbirths are 212, and total live births are 12450, what is the Neonatal Mortality Rate (NMR)?

Accepted Answer

36

Answer

17

Answer

64

Answer

92

Biostatistics — MCQs

Biostatistics — MCQs

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