Biostatistics Practice Questions

Q: What is a disadvantage of a case-control study when compared to a cohort study?

It cannot measure the cause-to-effect relationship.. ### Explanation **Why the correct answer is right:** The primary disadvantage of a **case-control study** is its inability to establish a definitive **temporal relationship** (cause-to-effect). In a case-control study, we start with the outcome (the disease) and look backward in time to identify exposures. Because both the exposure and the outcome have already occurred at the time the study begins, it is difficult to prove that the exposure preceded the disease. In contrast, a **cohort study** starts with a healthy population, identifies the exposure first, and follows them forward to see who develops the disease, making it the gold standard for establishing temporality and calculating **Incidence**. **Analysis of Incorrect Options:** * **A & B (Easy, Rapid, and Inexpensive):** These are actually **advantages** of case-control studies. Since they do not require long-term follow-up or large sample sizes, they are more resource-efficient than cohort studies. * **D (Useful in rare diseases):** This is another major **advantage**. Because you can specifically recruit "cases" of a rare condition, you don't have to wait years for the disease to manifest in a general population (as you would in a cohort study). **High-Yield NEET-PG Pearls:** * **Measure of Association:** Case-control studies use **Odds Ratio (OR)**; Cohort studies use **Relative Risk (RR)** and **Attributable Risk (AR)**. * **Directionality:** Case-control is **Retrospective** (Effect to Cause); Cohort is usually **Prospective** (Cause to Effect). * **Bias:** Case-control studies are highly prone to **Recall Bias** and **Selection Bias**. * **Incidence:** You **cannot** calculate Incidence from a case-control study; it can only be calculated from a cohort study.

Q: A case series report is a type of:

Observational study. ### Explanation **1. Why the Correct Answer is Right:** An **Observational study** is one where the investigator simply observes and records the natural course of events without any intervention or manipulation of the study subjects. A **Case Series** is a descriptive observational study that describes the characteristics of a group of patients with a common disease or exposure. Since the researcher is merely documenting clinical findings, outcomes, or unusual presentations in a group of patients without assigning a treatment or using a control group, it falls strictly under the umbrella of observational research. **2. Why the Other Options are Wrong:** * **Analytic study (A):** These studies (e.g., Case-Control, Cohort) are designed to test a hypothesis and determine the association between an exposure and an outcome using a **comparison group**. A case series lacks a control group, making it descriptive, not analytic. * **Experimental study (B) / Intervention study (D):** These terms are often used interchangeably (e.g., Randomized Controlled Trials). In these studies, the investigator actively **manipulates the exposure** (e.g., giving a drug) to observe the effect. In a case series, the "exposure" or "treatment" has already occurred or is happening naturally; the researcher does not control it. **3. NEET-PG High-Yield Pearls:** * **Hierarchy of Evidence:** Case series are higher in the hierarchy than Case Reports but lower than Case-Control and Cohort studies. * **Key Feature:** The hallmark of a Case Series is the **absence of a control group**. * **Usefulness:** They are the best study design for identifying **new/emerging diseases** or rare side effects (e.g., the first reports of Kaposi Sarcoma in HIV patients). * **Limitation:** They cannot be used to calculate "Relative Risk" or "Odds Ratio" because there is no comparison group.

Q: Which of the following statistical tests can be used to study ordinal data from two independent samples from a population that is not normally distributed?

The Wilcoxon rank-sum test. **Explanation:** The core of this question lies in identifying the correct **non-parametric test** for comparing two independent groups. **Why Option B is Correct:** The **Wilcoxon rank-sum test** (also known as the **Mann-Whitney U test**) is the non-parametric equivalent of the independent Student’s t-test. It is specifically used when: 1. The data is **ordinal** (ranked) or continuous but **not normally distributed**. 2. There are **two independent samples** (e.g., comparing pain scores between Group A and Group B). Since the question specifies non-normal distribution and ordinal data, this is the most appropriate choice. **Why Other Options are Incorrect:** * **A. Student’s t-test:** This is a **parametric test**. It requires the data to be continuous (interval/ratio scale) and follow a **normal distribution**. * **C. Chi-square test:** This test is used for **categorical (nominal) data** to compare proportions or test the association between two variables (e.g., Smoker vs. Non-smoker). * **D. One-way ANOVA:** This is a parametric test used to compare the means of **three or more** independent groups. Its non-parametric counterpart is the Kruskal-Wallis test. **High-Yield Clinical Pearls for NEET-PG:** * **Parametric vs. Non-Parametric:** If the data is "Normal," use Parametric. If "Skewed" or "Ordinal," use Non-Parametric. * **Wilcoxon Signed-Rank Test:** Do not confuse this with the Rank-Sum test. The *Signed-Rank* test is for **paired** data (e.g., before and after treatment in the same patient). * **Memory Aid:** **M**ann-Whitney **U** test = **U**npaired data; **W**ilcoxon **S**igned-rank = **S**ame person (Paired).

Q: In a cohort study, out of those exposed to a risk factor, 10 are diseased, and out of those not exposed to the risk factor, 5 are diseased. What is the relative risk?

2. ### Explanation **1. Understanding the Correct Answer (B: 2)** Relative Risk (RR), also known as Risk Ratio, is the primary measure of association in a **Cohort Study**. It compares the incidence of a disease among those exposed to a risk factor with the incidence among those not exposed. The formula for Relative Risk is: **RR = Incidence among exposed (Ie) / Incidence among non-exposed (Io)** In this question: * Incidence among exposed (Ie) = 10 * Incidence among non-exposed (Io) = 5 * **RR = 10 / 5 = 2** An RR of 2 indicates that the exposed group is twice as likely to develop the disease compared to the non-exposed group, suggesting a positive association between the risk factor and the disease. **2. Analysis of Incorrect Options** * **Option A (1.33):** This is a mathematical error, likely arising from incorrect division or adding values incorrectly. * **Option C (0.5):** This is the inverse (5/10). An RR < 1 indicates a protective effect (e.g., a vaccine), which contradicts the data provided. * **Option D (50):** This is a calculation error, possibly from multiplying the values instead of dividing them. **3. NEET-PG High-Yield Pearls** * **Study Design:** RR is calculated in **Prospective Cohort Studies**. It cannot be calculated in Case-Control studies (where Odds Ratio is used instead). * **Interpretation:** * RR = 1: No association. * RR > 1: Positive association (Risk factor). * RR < 1: Negative association (Protective factor). * **Attributable Risk (AR):** Calculated as (Ie - Io) / Ie × 100. It indicates the amount of disease that can be prevented if the exposure is eliminated. * **Incidence:** Cohort studies are the only way to directly calculate the incidence of a disease.

Q: Which of the following is true regarding random error?

Error in sampling. ### Explanation **Correct Answer: C. Error in sampling** **Understanding Random Error** In biostatistics, **Random Error** (also known as sampling error) occurs due to chance variations that happen when a sample is taken from a population. Even with a perfectly designed study, a sample may not perfectly represent the population simply due to "the luck of the draw." * **Key Characteristic:** It is unpredictable and non-systematic. * **Mitigation:** Random error can be reduced by **increasing the sample size** (which narrows the confidence interval and increases precision). **Analysis of Incorrect Options:** * **Option A (Systematic differences):** This describes **Selection Bias**. Bias is a systematic error that results in an incorrect estimate of the association between exposure and outcome. * **Option B (Questions about past history):** This refers to **Recall Bias**, a type of information/measurement bias common in case-control studies where cases tend to remember past events more clearly than controls. * **Option D (Different rates of admission):** This is a specific type of selection bias known as **Berkson’s Bias** (Admission Rate Bias), occurring when hospital-based samples do not represent the general population. **High-Yield Pearls for NEET-PG:** 1. **Random Error vs. Bias:** Random error affects **Precision** (reliability); Systematic error (Bias) affects **Validity** (accuracy). 2. **P-value:** The p-value is the probability that the observed result occurred due to random error (chance) alone. 3. **Sample Size:** Increasing sample size reduces random error but has **no effect** on systematic bias. 4. **Types of Bias:** Always remember that Bias is an error in design/conduction, whereas Random Error is an error in sampling.

Q: All are true about the Chi-Square test except?

The lowest expected frequency should be less than 5.. ### Explanation The **Chi-Square ($\chi^2$) test** is a fundamental statistical tool used in medical research to analyze categorical data. **1. Why Option D is the correct answer (The Exception):** For a Chi-Square test to be valid, a key assumption is that the **expected frequency in any cell should not be less than 5**. If the expected frequency is less than 5, the test becomes inaccurate because the distribution no longer approximates the Chi-Square distribution. In such cases, **Fisher’s Exact Test** is preferred (for $2 \times 2$ tables) or **Yates’ Correction** is applied. **2. Analysis of Incorrect Options:** * **Option A:** Chi-Square is indeed a **non-parametric test** because it does not assume a normal distribution of the underlying population and deals with frequencies rather than mean/standard deviation. * **Option B:** It is the primary test used to assess the **association between qualitative (categorical) variables** (e.g., comparing the incidence of a disease between smokers and non-smokers). * **Option C:** Like most statistical tests, the validity of the Chi-Square test relies on the assumption that the **sample is randomly selected** and observations are independent. **3. NEET-PG High-Yield Pearls:** * **Degrees of Freedom (df):** Calculated as $(r-1) \times (c-1)$, where $r$ = rows and $c$ = columns. * **Null Hypothesis ($H_0$):** Assumes there is no association between the variables. * **Fisher’s Exact Test:** Use this instead of Chi-Square when the sample size is very small or expected cell frequency is $<5$. * **McNemar’s Test:** A variation of Chi-Square used for **paired data** (e.g., before-and-after studies).

Q: Which measure of central tendency is best determined for the following set of data: 18, 20, 22, 24, 26, 28, 30?

Mean. **Explanation:** The correct answer is **Mean (Arithmetic Average)**. **1. Why Mean is the Correct Choice:** The provided data set (18, 20, 22, 24, 26, 28, 30) follows a **Normal (Symmetrical) Distribution**. The values are evenly spaced with a constant difference of 2, and there are no extreme values or outliers. In a normal distribution, the **Mean** is the most preferred and powerful measure of central tendency because it utilizes every value in the data set and is mathematically stable for further statistical analysis (like calculating Standard Deviation). **2. Why Other Options are Incorrect:** * **Median:** While the median (24) is also accurate here, it is primarily the "measure of choice" for **skewed distributions** or data containing outliers (e.g., income levels or incubation periods), as it is not affected by extreme values. * **Mode:** The mode is the most frequently occurring value. In this data set, every value appears only once (no mode). Mode is best suited for **nominal/categorical data** (e.g., most common blood group). **3. High-Yield Clinical Pearls for NEET-PG:** * **Normal Distribution:** Mean = Median = Mode. The Mean is the best measure. * **Skewed Distribution:** The **Median** is the best measure of central tendency. * **Qualitative Data:** The **Mode** is the best measure. * **Most Sensitive to Outliers:** The Mean (it shifts towards the tail). * **Least Sensitive to Outliers:** The Mode and Median. * **Relationship in Positive Skew:** Mean > Median > Mode. * **Relationship in Negative Skew:** Mean < Median < Mode.

Q: What does it indicate when the highest mean and lowest mode are observed?

Positive skewing. ### Explanation In biostatistics, the relationship between the measures of central tendency (Mean, Median, and Mode) determines the shape of the distribution curve. **1. Why Positive Skewing is Correct:** In a **Positively Skewed Distribution** (also known as Right-skewed), the tail of the distribution extends toward the higher values on the right side. * The **Mode** represents the peak of the curve (the most frequent value) and remains at the lowest numerical value. * The **Mean** is highly sensitive to extreme values (outliers) in the tail, which pulls it toward the right. * Therefore, the relationship is: **Mean > Median > Mode**. Since the Mean is the highest and the Mode is the lowest, this perfectly describes positive skewing. **2. Why the Other Options are Incorrect:** * **Negative Skewing (Left-skewed):** The tail extends toward the lower values. Here, the Mean is pulled down by outliers, resulting in **Mode > Median > Mean**. The Mean is the lowest value, not the highest. * **Normal/Symmetrical Distribution:** In a perfectly symmetrical bell-shaped curve, the Mean, Median, and Mode are all equal (**Mean = Median = Mode**). There is no "highest" or "lowest" value among them. **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **Memory Aid:** In **P**ositive skew, the Mean is more **P**ositive (greater). In **N**egative skew, the Mean is more **N**egative (smaller). * **Median:** Always stays in the middle in both types of skewed distributions. It is the best measure of central tendency for skewed data. * **Sensitivity:** The Mean is the most affected by outliers; the Mode is the least affected. * **Example:** Income distribution in a population or incubation periods of most infectious diseases usually follow a positive skew.

Question 1

What is a disadvantage of a case-control study when compared to a cohort study?

Accepted Answer

It cannot measure the cause-to-effect relationship.

Answer

It is easy to carry out.

Answer

It is rapid and inexpensive.

Answer

It is useful in rare diseases.

Question 2

A case series report is a type of:

Accepted Answer

Observational study

Answer

Analytic study

Answer

Experimental study

Answer

Intervention study

Question 3

Which of the following statistical tests can be used to study ordinal data from two independent samples from a population that is not normally distributed?

Accepted Answer

The Wilcoxon rank-sum test

Answer

The Student's t-test

Answer

The Chi-square test

Answer

The one-way analysis of variance

Question 4

In a cohort study, out of those exposed to a risk factor, 10 are diseased, and out of those not exposed to the risk factor, 5 are diseased. What is the relative risk?

Accepted Answer

2

Answer

1.33

Answer

0.5

Answer

50

Question 5

Which of the following is true regarding random error?

Accepted Answer

Error in sampling

Answer

Systematic differences in characteristic between cases and control

Answer

Cases and controls are asked questions about their past history

Answer

Different rates of admission in hospital

Question 6

All are true about the Chi-Square test except?

Accepted Answer

The lowest expected frequency should be less than 5.

Answer

It is a non-parametric test of significance.

Answer

It is used to test the significance of association between two or more qualitative characteristics.

Answer

The sample must be randomly selected.

Question 7

Which measure of central tendency is best determined for the following set of data: 18, 20, 22, 24, 26, 28, 30?

Accepted Answer

Mean

Answer

Median

Answer

Mode

Answer

None

Question 8

Which of the following is NOT true about a cohort study?

Accepted Answer

Is time-consuming.

Answer

Requires a large number of subjects.

Answer

Is expensive.

Answer

Is suitable for studying common diseases.

Question 9

On a given day, a hospital had 50 admissions with about 20 girls and 30 boys. Out of these, 10 girls and 20 boys needed surgery. What is the probability of picking a person requiring surgery?

Accepted Answer

2/5

Answer

1/3

Answer

1/2

Answer

3/5

Question 10

What does it indicate when the highest mean and lowest mode are observed?

Accepted Answer

Positive skewing

Answer

Negative skewing

Answer

Normal distribution

Answer

Symmetrical distribution

Biostatistics — MCQs

Biostatistics — MCQs

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