Biostatistics Practice Questions

Q: What is the incidence of a disease in a population of 30,000 with 300 new cases?

10 per 1000. ### Explanation **1. Why the Correct Answer is Right** Incidence is defined as the number of **new cases** occurring in a defined population during a specific period. The formula for calculating the Incidence Rate is: $$\text{Incidence} = \frac{\text{Number of new cases}}{\text{Population at risk}} \times 1000$$ Plugging in the values from the question: $$\text{Incidence} = \frac{300}{30,000} \times 1000$$ $$\text{Incidence} = 0.01 \times 1000 = \mathbf{10 \text{ per } 1000}$$ This calculation reflects the frequency of the occurrence of the disease over time, making **Option B** the correct choice. **2. Why the Other Options are Wrong** * **Option A (0.1 per 1000):** This is a calculation error where the decimal point was misplaced (300/3,000,000). * **Option C (100 per 1000):** This represents a 10% incidence rate (3,000/30,000), which is ten times higher than the actual data provided. * **Option D (1 per 1000):** This would be the result if there were only 30 new cases instead of 300. **3. NEET-PG High-Yield Pearls** * **Denominator:** Always remember that the denominator for Incidence is the "Population at Risk." It excludes people who already have the disease or are immune. * **Study Design:** Incidence is best measured using a **Cohort Study**. * **Incidence vs. Prevalence:** While Incidence tracks *new* cases (water flowing into a tub), Prevalence tracks *all* existing cases (total water in the tub). * **Relationship:** $\text{Prevalence} = \text{Incidence} \times \text{Mean Duration of disease } (P = I \times D)$. * **Attack Rate:** This is a type of incidence rate used specifically during an epidemic, usually expressed as a percentage.

Q: What is the hypothesised null value for the confidence interval of relative risk?

One. ### Explanation **1. Why the Correct Answer is Right (The Concept of Ratios)** Relative Risk (RR) is a **ratio** of two probabilities (Incidence in exposed / Incidence in non-exposed). In biostatistics, the "null value" represents the point of "no difference" or "no effect." * If the risk in the exposed group is exactly the same as the risk in the non-exposed group, the ratio is **1** (e.g., 5% / 5% = 1). * Therefore, if a 95% Confidence Interval (CI) for Relative Risk includes the value **1**, the results are considered statistically non-significant (p > 0.05), as the null hypothesis cannot be rejected. **2. Why the Incorrect Options are Wrong** * **Option A (Zero):** Zero is the null value for measures based on **subtraction (differences)**, such as Mean Difference or Attributable Risk. If the difference between two groups is 0, there is no effect. For ratios, a value of 0 would imply the numerator is zero, which is not the null state. * **Options C & D (Two and Five):** These are arbitrary positive integers. They represent a doubling or quintupling of risk, respectively, indicating a strong positive association rather than a null effect. **3. Clinical Pearls & High-Yield Facts for NEET-PG** * **Null Value for Ratios:** For Relative Risk (RR), Odds Ratio (OR), and Hazard Ratio (HR), the null value is always **1**. * **Null Value for Differences:** For Mean Difference, Risk Difference, and Attributable Risk, the null value is always **0**. * **Significance Testing via CI:** * If the CI for RR is **(0.5 – 0.8)**: Significant (Protective effect, does not cross 1). * If the CI for RR is **(1.2 – 2.5)**: Significant (Risk factor, does not cross 1). * If the CI for RR is **(0.8 – 1.5)**: **Not significant** (Crosses 1).

Q: A diagnostic test for a particular disease has a sensitivity of 0.90 and a specificity of 0.80. A single test is applied to each subject in the population in which the diseased population is 30%. What is the probability that a person, negative to this test, has no disease?

95%. ### Explanation The question asks for the probability that a person who tests negative actually has no disease. In biostatistics, this is the definition of **Negative Predictive Value (NPV)**. #### 1. Why the Correct Answer (C) is Right To calculate NPV, we can use the formula or a hypothetical population grid (e.g., 1,000 people). * **Prevalence:** 30% (300 diseased, 700 healthy) * **Sensitivity:** 0.90 (True Positives = 0.90 × 300 = 270; False Negatives = 30) * **Specificity:** 0.80 (True Negatives = 0.80 × 700 = 560; False Positives = 140) **NPV Formula:** $$\text{NPV} = \frac{\text{True Negatives}}{\text{True Negatives} + \text{False Negatives}}$$ $$\text{NPV} = \frac{560}{560 + 30} = \frac{560}{590} \approx 0.949 \text{ or } \mathbf{95\%}$$ The high specificity and the relatively low prevalence contribute to a high NPV, ensuring that a negative result is highly reliable for ruling out the disease. #### 2. Why Other Options are Wrong * **A (Less than 50%):** This would occur only if the test had extremely low specificity or if the disease prevalence was near 100%. * **B (70%):** This is the percentage of healthy people in the population (1 - Prevalence), not the NPV. * **D (72%):** This is a common distractor resulting from miscalculating the denominator or confusing the formula with the Likelihood Ratio. #### 3. Clinical Pearls for NEET-PG * **NPV vs. Prevalence:** NPV is **inversely proportional** to prevalence. As prevalence decreases, NPV increases (it is easier to rule out a rare disease). * **PPV vs. Prevalence:** Positive Predictive Value (PPV) is **directly proportional** to prevalence. * **Screening vs. Diagnosis:** Sensitivity and NPV are crucial for **screening tests** (to "rule out"), while Specificity and PPV are crucial for **confirmatory tests** (to "rule in"). * **Mnemonic:** **SNOUT** (Sensitivity/Negative result/Rules OUT) and **SPIN** (Specificity/Positive result/Rules IN).

Q: A screening test is considered more sensitive if it has which of the following characteristics?

Few false negatives. **Explanation:** **Sensitivity** is defined as the ability of a screening test to correctly identify those who actually have the disease (True Positives). Mathematically, it is calculated as: **Sensitivity = [TP / (TP + FN)] × 100** **Why Option A is Correct:** Sensitivity is inversely related to the number of **False Negatives (FN)**. A highly sensitive test is "good at catching cases"; therefore, it rarely misses people who have the disease. If a test has "few false negatives," the denominator (TP + FN) stays close to the numerator (TP), resulting in a value near 100%. In clinical practice, a highly sensitive test is used for **screening** because a negative result effectively "rules out" the disease (SNOUT). **Why Other Options are Incorrect:** * **B. Few false positives:** This is a characteristic of **Specificity**. A test with few false positives is good at "ruling in" a disease (SPIN). * **C. More false positives:** This would decrease the Specificity and the Positive Predictive Value (PPV) of the test, but it does not directly define sensitivity. * **D. More false negatives:** This would significantly **decrease** sensitivity, as the test would miss many diseased individuals. **High-Yield Clinical Pearls for NEET-PG:** * **SNOUT:** **S**ensitivity **N**egative result rules **OUT** the disease. * **SPIN:** **S**pecificity **P**ositive result rules **IN** the disease. * **Screening vs. Diagnosis:** Screening tests require high **Sensitivity** (to avoid missing cases), while confirmatory/diagnostic tests require high **Specificity** (to avoid false labeling). * **Ideal Test:** Has 100% Sensitivity and 100% Specificity (rare in practice).

Q: Regarding the normal curve, which of the following is true?

The curve is bilaterally symmetrical. ### Explanation The **Normal Distribution (Gaussian Distribution)** is a fundamental concept in biostatistics used to describe how continuous variables (like height, blood pressure, or hemoglobin levels) are distributed in a large population. **1. Why the Correct Answer is Right:** The normal curve is defined by its characteristic **bell shape**. It is **bilaterally symmetrical** about its center. This symmetry occurs because the mean, median, and mode are all equal and located at the peak of the curve. If you were to fold the curve at the mean, the two halves would overlap perfectly. **2. Why the Incorrect Options are Wrong:** * **Options A & B (Skewness):** A normal curve has **zero skewness**. If a curve is "skewed to the right" (positive skew), the tail is longer on the right side (Mean > Median). If "skewed to the left" (negative skew), the tail is longer on the left (Mean < Median). * **Option C (Touching the baseline):** The normal curve is **asymptotic** to the baseline. This means the "tails" or limbs of the curve extend to infinity in both directions but theoretically never actually touch or cross the horizontal x-axis. **3. High-Yield Clinical Pearls for NEET-PG:** * **The 68-95-99.7 Rule (Empirical Rule):** * Mean ± 1 SD covers **68.2%** of values. * Mean ± 2 SD covers **95.4%** of values. * Mean ± 3 SD covers **99.7%** of values. * **Standard Normal Curve:** A specific type of normal curve where the **Mean = 0** and **Standard Deviation = 1**. * **Total Area:** The total area under the normal curve is always equal to **1 (or 100%)**. * **Point of Inflection:** The point where the curve changes from convex to concave occurs at **Mean ± 1 SD**.

Q: Systolic blood pressure of a group of 200 people follows a normal distribution with a mean BP of 120 mm Hg and a standard deviation of 10 mm Hg. Which of the following is true?

68% of people have BP between 110-130 mm Hg. ### Explanation This question tests your understanding of the **Normal Distribution (Gaussian Curve)** and the **Empirical Rule**, which is a high-yield topic in Biostatistics. #### Why Option A is Correct In a normal distribution, data is symmetrically distributed around the mean. The Empirical Rule states: * **Mean ± 1 Standard Deviation (SD)** covers approximately **68.2%** of the values. * **Mean ± 2 SD** covers approximately **95.4%** of the values. * **Mean ± 3 SD** covers approximately **99.7%** of the values. Given: Mean = 120 mm Hg and SD = 10 mm Hg. * Calculation for 1 SD: $120 \pm 10 = 110$ to $130$ mm Hg. * Therefore, 68% of the population falls within the 110–130 mm Hg range. #### Why Other Options are Incorrect * **Option B:** 95% of the people would fall within Mean ± 2 SD ($120 \pm 20$), which is **100–140 mm Hg**, not 110–130. * **Option C:** As calculated above, the range of 100–140 mm Hg corresponds to 95% of the population, not 68%. * **Option D:** 99% (specifically 99.7%) of the people would fall within Mean ± 3 SD ($120 \pm 30$), which is **90–150 mm Hg**. #### High-Yield Clinical Pearls for NEET-PG 1. **Symmetry:** In a perfectly normal distribution, **Mean = Median = Mode**. 2. **Z-Score:** This represents the number of standard deviations a value is from the mean. A BP of 130 in this example has a Z-score of +1. 3. **Standard Error (SE):** Do not confuse SD with SE. $SE = SD / \sqrt{n}$. SE is used to calculate Confidence Intervals, while SD describes the dispersion of individual data points. 4. **Skewness:** If the mean is greater than the median, it is a **Positively Skewed** distribution (tail to the right). If the mean is less than the median, it is **Negatively Skewed** (tail to the left).

Q: Which of the following is the best method of randomization?

Random number table. ### Explanation **Randomization** is the "heart" of a Randomized Controlled Trial (RCT). Its primary purpose is to eliminate **selection bias** and ensure that both known and unknown confounding factors are distributed equally between the study and control groups, making them comparable at baseline. **Why Option D is Correct:** The **Random Number Table** (e.g., Tippett’s table or Fisher and Yates table) is traditionally considered the "gold standard" and the most reliable manual method for randomization in clinical research. It ensures that every participant has an equal, non-zero, and independent chance of being assigned to any group. In the context of standard medical examinations like NEET-PG, it is prioritized as the most robust method for ensuring true randomness. **Analysis of Incorrect Options:** * **Option A (Computer-generated):** While widely used in modern large-scale trials for convenience, traditional biostatistics textbooks still emphasize the Random Number Table as the fundamental "best" method for academic testing purposes. * **Option B (Odd/even day admission):** This is **Quasi-randomization** (Systematic sampling). It is predictable; researchers know which patient will go into which group tomorrow, leading to potential selection bias. * **Option C (Lottery method):** While a form of random allocation, it is primitive, difficult to document/audit, and prone to manipulation or physical bias (e.g., improper mixing of chits). **High-Yield Clinical Pearls for NEET-PG:** * **Purpose of Randomization:** To eliminate selection bias and ensure **comparability** of groups. * **Blinding:** Eliminates **ascertainment (observer) bias**. * **Concealment of Allocation:** Prevents the researcher from knowing the sequence before assignment; it is the step that *protects* the randomization process. * **Sequence:** Randomization $\rightarrow$ Allocation Concealment $\rightarrow$ Blinding.

Q: If 10 women use an IUCD for 10 years, and assuming the failure rate of IUCD is 2 per 100 women-years (HWY), what is the number of expected accidental pregnancies?

2. ### Explanation **1. Understanding the Concept (Pearl Index)** The failure rate of contraceptives is measured using the **Pearl Index**, expressed as the number of accidental pregnancies per 100 women-years (HWY) of exposure. To calculate the expected pregnancies, we use the formula: $$\text{Total Pregnancies} = \frac{\text{Failure Rate} \times \text{Total Women-Years}}{100}$$ **Step-by-step Calculation:** * **Total Women-Years:** 10 women $\times$ 10 years = **100 women-years**. * **Failure Rate:** Given as 2 per 100 women-years. * **Calculation:** $\frac{2 \times 100}{100} = \mathbf{2}$ **pregnancies.** **2. Analysis of Incorrect Options** * **Option A (1):** This would be the result if the total exposure was only 50 women-years (e.g., 5 women for 10 years). * **Option C (5):** This would imply a failure rate of 5 per 100 HWY, which is higher than the standard failure rate for modern IUCDs like Cu-T 380A. * **Option D (20):** This error usually occurs if one forgets to divide by the "100" in the HWY denominator, simply multiplying 10 women $\times$ 2 (rate). **3. Clinical Pearls for NEET-PG** * **Pearl Index Definition:** It is the most common method to compare the efficacy of contraceptive methods. * **Denominator:** Always remember the denominator is **100 women-years** (1,200 months of exposure). * **Typical vs. Perfect Use:** The Pearl Index for **Cu-T 380A** is approximately 0.8 per 100 HWY (very effective), while for Oral Contraceptive Pills, "typical use" failure is around 9 per 100 HWY. * **Lowest Failure Rate:** Currently, the **Subdermal Implant (Nexplanon)** has the lowest Pearl Index (~0.05).

Question 1

What is the incidence of a disease in a population of 30,000 with 300 new cases?

Accepted Answer

10 per 1000

Answer

0.1 per 1000

Answer

100 per 1000

Answer

1 per 1000

Question 2

What is the hypothesised null value for the confidence interval of relative risk?

Accepted Answer

One

Answer

Zero

Answer

Two

Answer

Five

Question 3

A diagnostic test for a particular disease has a sensitivity of 0.90 and a specificity of 0.80. A single test is applied to each subject in the population in which the diseased population is 30%. What is the probability that a person, negative to this test, has no disease?

Accepted Answer

95%

Answer

Less than 50%

Answer

70%

Answer

72%

Question 4

A screening test is considered more sensitive if it has which of the following characteristics?

Accepted Answer

Few false negatives

Answer

Few false positives

Answer

More false positives

Answer

More false negatives

Question 5

Regarding the normal curve, which of the following is true?

Accepted Answer

The curve is bilaterally symmetrical

Answer

There is a skew to the right

Answer

There is a skew to the left

Answer

Both limbs of the curve touch the baseline

Question 6

A study was conducted to assess the role of radiation in leukemia. Among 100 patients with leukemia, 70 reported a history of radiation exposure. Among 100 controls, 30 reported a history of radiation exposure. What type of bias is most likely present in this study?

Accepted Answer

Recall bias

Answer

Reporting bias

Answer

Interviewer bias

Answer

Hawthorne bias

Question 7

Systolic blood pressure of a group of 200 people follows a normal distribution with a mean BP of 120 mm Hg and a standard deviation of 10 mm Hg. Which of the following is true?

Accepted Answer

68% of people have BP between 110-130 mm Hg

Answer

95% of people have BP between 110-130 mm Hg

Answer

68% of people have BP between 100-140 mm Hg

Answer

99% of people have BP between 100-140 mm Hg

Question 8

Which of the following is the best method of randomization?

Accepted Answer

Random number table

Answer

Computer-generated randomization

Answer

Odd/even day hospital admission

Answer

Lottery method

Question 9

If 10 women use an IUCD for 10 years, and assuming the failure rate of IUCD is 2 per 100 women-years (HWY), what is the number of expected accidental pregnancies?

Accepted Answer

2

Answer

1

Answer

5

Answer

20

Question 10

In a community of 3000 people, 80% are Hindus, 10% Muslims, 5% Sikh, 4% Christians, and 1% Jains. To select a sample of 300 people to analyze food habits, what would be the ideal sampling method?

Accepted Answer

Stratified random sampling

Answer

Simple random sampling

Answer

Systematic random sampling

Answer

Inverse sampling

Biostatistics — MCQs

Biostatistics — MCQs

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