Biostatistics Practice Questions

Q: A town in the hills had a mid-year population of 250000 in the year 2021. During the same year, the death registry of the town recorded 1500 deaths due to tuberculosis in 365 calendar days. Given these facts, what is the specific death rate for tuberculosis in the town?

6. ***6*** - The **specific death rate** (also called cause-specific death rate) is calculated as **(Number of deaths from a specific cause / Mid-year population) × 1000** - For tuberculosis: (1500 deaths / 250,000 population) × 1000 = **6 deaths per 1000 population** - This represents the tuberculosis mortality rate in the community *10* - This incorrect value would be obtained if there were 2500 deaths due to tuberculosis instead of 1500 - Calculation error: (2500 / 250,000) × 1000 = 10 - Always verify the numerator (number of deaths) from the given data *60* - This is a common calculation error resulting from incorrect multiplication factor - May occur if using: (1500 / 250,000) × 10,000 = 60 (wrong multiplier) - Remember: specific death rate uses **per 1000** population, not per 10,000 *1* - This value results from calculation errors or incorrect rounding - May occur if dividing by wrong population figure or forgetting the multiplier - The actual rate (1500/250,000 = 0.006) must be multiplied by 1000 to get rate per 1000 population

Q: In a town, a study was carried out to determine the role of cigarette smoking in causation of lung cancer. It was found that a total of 7000 people in the town were smokers. Of them, 70 developed lung cancer. In the same town, 3000 people were non-smokers. Of them, 3 developed lung cancer. Given these numbers, what would be the attributable risk to cigarette smoking for lung cancer?

90%. **90%** - The **attributable risk** (AR) is calculated as the incidence in the exposed group minus the incidence in the unexposed group, divided by the incidence in the exposed group, all multiplied by 100%. - Incidence in smokers (Ie) = 70 cases / 7000 smokers = 0.01. Incidence in nonsmokers (Io) = 3 cases / 3000 nonsmokers = 0.001. AR = ((0.01 - 0.001) / 0.01) * 100% = (0.009 / 0.01) * 100% = 0.9 * 100% = **90%**. *30%* - This percentage is incorrect; it does not align with the formal calculation of **attributable risk** based on the given incidence rates in exposed and unexposed groups. - A value of 30% would imply a much smaller difference in incidence between smokers and non-smokers relative to the incidence in smokers. *10%* - This value might be obtained if the calculation confused **attributable risk** with the proportion of cases in the unexposed group or some other miscalculation. - It significantly underestimates the proportion of lung cancer in smokers directly attributable to their smoking status. *60%* - This answer is incorrect as it does not result from the appropriate application of the **attributable risk formula**. - The discrepancy between the incidence rate in smokers (0.01) and non-smokers (0.001) is much higher than what would lead to a 60% attributable risk.

Q: Forty patients with diarrhoeal diseases were studied. Their age distribution is given in the table below : What is the mean age of the patients in this study?

5 years. ***5 years*** - To calculate the **mean age** from grouped data, first find the midpoint of each age range. - The midpoints are: **2** for 0-4 years (22 patients), **7** for 5-9 years (12 patients), and **12** for 10-14 years (6 patients). - Multiply each midpoint by the number of patients in that range: (2 × 22) + (7 × 12) + (12 × 6) = 44 + 84 + 72 = **200**. - Divide the sum of these products by the total number of patients (**40**) to get the mean age: **200 / 40 = 5 years**. *2 years* - This is the **midpoint** of the first age group (0-4 years), not the mean of the entire dataset. - While 22 patients (the majority) fall in this age group, the mean must account for the **weighted distribution** across all age groups. - This would only be correct if all 40 patients were in the 0-4 years age group. *4 years* - This answer suggests an **incorrect calculation** of the weighted mean or an error in summing the products. - It does not match the correct weighted mean formula: Σ(midpoint × frequency) / total frequency. - May result from miscalculating the sum (200) or the total number of patients. *6 years* - This value is higher than the calculated mean and likely results from a **mathematical error**. - The correct calculation yields 5 years, not 6 years. - This might arise from rounding errors or incorrect midpoint selection.

Q: In a normal curve, the area of one standard deviation around the mean includes which of the following per cent of values in a distribution ?

68.3%. ***68.3%*** - In a **normal distribution**, approximately 68.3% of the data falls within **one standard deviation** (±1σ) of the mean. - This is a fundamental property of the **empirical rule** (68-95-99.7 rule) applied to normally distributed data. *95.4%* - This percentage represents the data within **two standard deviations** (±2σ) of the mean in a normal distribution. - It is often rounded to 95% for confidence intervals, but the precise value is 95.4%. *48.6%* - This value does not correspond to a standard interval around the mean in a **normal distribution**. - It might be a distracter derived from incorrectly calculating or misremembering the percentages of the empirical rule. *99.7%* - This percentage represents the data within **three standard deviations** (±3σ) of the mean in a normal distribution. - It indicates that almost all data points in a normal curve lie within this range.

Q: To understand the relationship between weight and height of a group of school children, the data can graphically be best depicted through

Scatter diagram. ***Scatter diagram*** - A **scatter diagram** (also called a scatter plot) is ideal for showing the relationship between **two continuous variables**, such as weight and height. - Each point on the graph represents an individual's paired values for weight and height, allowing visual identification of **patterns or correlations**. *Histogram* - A **histogram** is used to display the distribution of **a single continuous variable**, showing the frequency of data points within specific intervals or bins. - It would not effectively demonstrate the **relationship or correlation** between two variables simultaneously. *Bar diagram* - A **bar diagram** (or bar chart) is typically used for comparing **categorical data** or discrete values, showing frequencies or proportions for different categories. - It is not suitable for visualizing the relationship between **two continuous numerical variables** like weight and height. *Pictogram* - A **pictogram** uses images or symbols to represent data, often used for presenting simple statistics to a general audience. - It is generally used for **categorical data** or simple comparisons and lacks the precision needed to display the continuous relationship between weight and height.

Q: For a screening test, 90% specificity means that 90% of non-diseased persons will give a

True negative result. ***True negative result*** - **Specificity** is defined as the proportion of **true negatives** among individuals **without the disease**. - A 90% specificity means that 90% of healthy individuals will correctly test negative for the disease. *False negative result* - A **false negative** occurs when a diseased person tests negative, which is related to the concept of **sensitivity**, not specificity. - This would imply missing actual cases of the disease. *True positive result* - A **true positive** occurs when a diseased person tests positive, which is also related to **sensitivity**. - This indicates accurate detection of the disease in affected individuals. *False positive result* - A **false positive** occurs when a non-diseased person inappropriately tests positive. - If 90% of non-diseased persons give a negative result (true negative), then 10% would give a **false positive result**.

Q: What is the method of sampling in which the units are picked up at regular intervals from the universe ?

Systematic random sampling. ***Systematic random sampling*** - This method involves selecting samples at a **fixed and regular interval** from a larger population after a random starting point is chosen. - It ensures representation across the entire population list by picking every nth unit, making it **efficient for large datasets**. *Stratified random sampling* - This method involves dividing the population into **homogeneous subgroups** (strata) and then drawing a random sample from each stratum. - It is used when there is a need to ensure **representation of specific subgroups**, which is not the primary characteristic described. *Snow-ball sampling* - This is a **non-probability sampling technique** where initial subjects recruit future subjects from among their acquaintances, typically used for hard-to-reach populations. - It relies on existing social networks and is not characterized by picking units at regular intervals. *Simple random sampling* - In this method, every member of the population has an **equal chance of being selected**, and selections are made fully at random. - While random, it does not involve the specific process of picking units at **regular, predetermined intervals**.

Q: A randomized trial comparing the efficacy of two drugs showed a difference between the two (p value < 0.05). However, in reality the drugs do not differ. This is an example of

Type I error. ***Type I error*** - A **Type I error** occurs when the **null hypothesis is incorrectly rejected**, leading to the conclusion that a significant difference exists when, in reality, there is no true difference. - In this scenario, the trial concluded a difference (p < 0.05), but the drugs are truly equivalent, which is precisely the definition of a **Type I error**. *Both type I and II error* - It is impossible to commit both a **Type I** and a **Type II error** simultaneously for the same statistical test. - A **Type I error** involves rejecting a true null hypothesis, while a **Type II error** involves failing to reject a false null hypothesis. *Random error* - **Random error** refers to unpredictable fluctuations in measurements or results, which can be minimized but not eliminated. - While random error can contribute to variability in data, it is not the direct statistical error of concluding a non-existent difference when analyzing the results, which is a **Type I error**. *Type II error* - A **Type II error** occurs when the **null hypothesis is incorrectly accepted** (or not rejected), meaning a real difference exists but the study fails to detect it. - This scenario describes the opposite: a difference was detected and concluded, but it was false.

Q: In a village, every fifth house was selected for a study. This is an example of

Systematic random sampling. ***Systematic random sampling*** - This method involves selecting subjects from a **ordered sampling frame** at regular intervals, such as every k-th item. - In this scenario, selecting every fifth house represents a fixed interval (k=5), which is characteristic of systematic random sampling. *Simple random sampling* - This method ensures that every member of the population has an **equal chance of being selected**, often through random number generation. - It does not involve a predetermined, fixed interval of selection from an ordered list. *Convenience sampling* - This technique involves selecting subjects who are **easily accessible or readily available**, without any systematic or random process. - It is prone to bias as it does not represent the entire population. *Stratified random sampling* - This method involves dividing the population into **homogeneous subgroups (strata)** and then conducting simple random sampling within each stratum. - The scenario does not describe dividing the village households into distinct subgroups before selection.

Q: The ability of a test to identify correctly those who do not have the disease is called its

Specificity. ***Specificity*** - **Specificity** is the proportion of **true negatives** correctly identified by the test. - It measures the ability of a test to correctly identify individuals who **do not have the disease**. *Sensitivity* - **Sensitivity** is the proportion of **true positives** correctly identified by the test. - It measures the ability of a test to correctly identify individuals who **do have the disease**. *Positive predictive value* - **Positive predictive value (PPV)** is the probability that a patient with a **positive test result** actually has the disease. - It depends on the **prevalence** of the disease in the population being tested. *Negative predictive value* - **Negative predictive value (NPV)** is the probability that a patient with a **negative test result** actually does not have the disease. - It also depends on the **prevalence** of the disease in the population.

Question 1

A town in the hills had a mid-year population of 250000 in the year 2021. During the same year, the death registry of the town recorded 1500 deaths due to tuberculosis in 365 calendar days. Given these facts, what is the specific death rate for tuberculosis in the town?

Accepted Answer

6

Answer

10

Answer

60

Answer

1

Question 2

In a town, a study was carried out to determine the role of cigarette smoking in causation of lung cancer. It was found that a total of 7000 people in the town were smokers. Of them, 70 developed lung cancer. In the same town, 3000 people were non-smokers. Of them, 3 developed lung cancer. Given these numbers, what would be the attributable risk to cigarette smoking for lung cancer?

Accepted Answer

90%

Answer

30%

Answer

10%

Answer

60%

Question 3

Forty patients with diarrhoeal diseases were studied. Their age distribution is given in the table below : What is the mean age of the patients in this study?

Accepted Answer

5 years

Answer

4 years

Answer

6 years

Answer

2 years

Question 4

In a normal curve, the area of one standard deviation around the mean includes which of the following per cent of values in a distribution ?

Accepted Answer

68.3%

Answer

95.4%

Answer

48.6%

Answer

99.7%

Question 5

To understand the relationship between weight and height of a group of school children, the data can graphically be best depicted through

Accepted Answer

Scatter diagram

Answer

Histogram

Answer

Bar diagram

Answer

Pictogram

Question 6

For a screening test, 90% specificity means that 90% of non-diseased persons will give a

Accepted Answer

True negative result

Answer

False negative result

Answer

True positive result

Answer

False positive result

Question 7

What is the method of sampling in which the units are picked up at regular intervals from the universe ?

Accepted Answer

Systematic random sampling

Answer

Stratified random sampling

Answer

Snow-ball sampling

Answer

Simple random sampling

Question 8

A randomized trial comparing the efficacy of two drugs showed a difference between the two (p value < 0.05). However, in reality the drugs do not differ. This is an example of

Accepted Answer

Type I error

Answer

Both type I and II error

Answer

Random error

Answer

Type II error

Question 9

In a village, every fifth house was selected for a study. This is an example of

Accepted Answer

Systematic random sampling

Answer

Simple random sampling

Answer

Convenience sampling

Answer

Stratified random sampling

Question 10

The ability of a test to identify correctly those who do not have the disease is called its

Accepted Answer

Specificity

Answer

Sensitivity

Answer

Positive predictive value

Answer

Negative predictive value

Biostatistics — MCQs

Biostatistics — MCQs

On this page

Practice by Chapter

Want unlimited practice?