Biostatistics Practice Questions

Q: What is the Standardized Mortality Ratio (SMR) for the hazardous industry workers (as compared to national population)?

120. ***120*** - To calculate the Standardized Mortality Ratio (SMR), we first need to calculate the **expected deaths** for the hazardous industry workers based on the national death rates. - The formula for expected deaths in each age group is: (National death rate / 1000) × Number of hazardous industry workers - For age group 25-34: Expected deaths = (2.0 / 1000) × 3000 = **6** - For age group 35-44: Expected deaths = (3.5 / 1000) × 2000 = **7** - For age group 45-54: Expected deaths = (6.0 / 1000) × 2000 = **12** - **Total expected deaths** = 6 + 7 + 12 = **25** - **Total observed deaths** = 8 + 9 + 13 = **30** - **SMR formula**: (Total Observed Deaths / Total Expected Deaths) × 100 - **SMR = (30 / 25) × 100 = 1.2 × 100 = 120** - This indicates that the hazardous industry workers have a **20% higher mortality rate** compared to the national population after age-standardization. *100* - An SMR of 100 would indicate that the observed mortality equals the expected mortality (no difference from the national average). - However, the observed deaths (30) exceed the expected deaths (25), so the SMR must be greater than 100. - This option represents the null value where there is no excess mortality. *110* - This option underestimates the actual SMR calculated from the data. - An SMR of 110 would suggest only a 10% excess mortality, which does not match the observed-to-expected ratio of 30:25. - The calculation clearly shows a ratio of 1.2, not 1.1. *130* - This option overestimates the SMR. - An SMR of 130 would require observed deaths to be 1.3 times the expected deaths (32.5 deaths expected for 30 observed). - The actual ratio is 30/25 = 1.2, making this value too high.

Q: Consider the following: 1. Standard deviation 2. Range 3. Mode 4. Median Among the above, which is/are the measure/measures of dispersion?

1 and 2 only. ***1 and 2 only*** - The **standard deviation** quantifies the average amount of variability or dispersion around the mean, representing how spread out the data points are. - The **range** is the difference between the maximum and minimum values in a dataset, providing a simple measure of the total spread. *3 and 4 only* - The **mode** represents the most frequently occurring value in a dataset, which is a measure of central tendency, not dispersion. - The **median** is the middle value when data is ordered, also a measure of central tendency. *1 only* - While **standard deviation** is a measure of dispersion, this option incorrectly excludes the **range**, which also quantifies data spread. - Both **standard deviation** and **range** are fundamental measures used to describe the variability within a dataset. *1, 2, 3 and 4* - This option incorrectly includes the **mode** and **median**, which are measures of **central tendency**, not dispersion. - Measures of dispersion specifically describe the **spread or variability** of data, whereas central tendency measures describe the center of the data.

Q: In a normal curve, the area between one standard deviation on either side of the mean will include:

Approximately 68% of the values. ***Approximately 68% of the values*** - In a **normal distribution** (bell curve), approximately **68% of data points** fall within one standard deviation ($\pm1\sigma$) from the mean. - This is a fundamental property of the **empirical rule** (or 68-95-99.7 rule) in statistics. *70 – 85% of the values* - This range is too broad and does not accurately reflect the specific percentage for **one standard deviation**. - While it overlaps with the correct value, it is not the precise percentage associated with $\pm1\sigma$. *95% of the values* - This percentage refers to the data included within **two standard deviations** ($\pm2\sigma$) from the mean in a normal distribution, not one. - The **empirical rule** states that approximately 95% of data falls within two standard deviations. *Less than 50% of the values* - This is incorrect, as the range of **one standard deviation** on either side of the mean covers more than half of the data. - The mean itself divides the data into two 50% halves, so incorporating any deviation around it will cover more than 50%.

Q: The relationship between birth rate and maternal hemoglobin is best studied by:

Correlation and regression.. ***Correlation and regression.*** - **Correlation** measures the strength and direction of a linear relationship between two quantitative variables (birth rate and maternal hemoglobin levels). - **Regression analysis** allows for modeling the relationship between variables, enabling prediction of birth rate based on maternal hemoglobin, or vice versa, and quantifying the effect of one on the other. *Sensitivity and specificity.* - These concepts are used to evaluate the performance of a **diagnostic test** or screening tool in correctly identifying individuals with and without a specific condition. - They are not appropriate for studying the relationship between two continuous variables like birth rate and maternal hemoglobin. *Standard error of difference between two means.* - This statistical measure is used to determine if there is a **statistically significant difference** between the means of two independent groups, typically when comparing a quantitative outcome between these groups. - It is not suitable for assessing the continuous relationship or association between two continuous variables. *Standard error of difference between two proportions.* - This measure is employed to assess whether there is a **statistically significant difference** between the proportions or percentages of an outcome in two different groups. - It is used for categorical data and is not applicable for analyzing the relationship between two continuous variables.

Q: Which of the following statements is true about direct age standardization?

A standard population is used. ***A standard population is used*** - In **direct age standardization**, age-specific death rates from the study population are applied to a **standard population's** age distribution to calculate an expected number of events. - This method helps to compare mortality or morbidity across different populations by removing the confounding effect of differing age structures. *Number of people in each age group is not known* - This statement is incorrect; to apply the study population's age-specific rates to the standard population, the **number of people in each age group of the standard population must be known**. - Without this demographic information, direct age standardization cannot be performed effectively. *Age specific death rates are not known* - This statement is incorrect because **age-specific death rates** for the *study population* are a prerequisite for direct age standardization. - These rates are multiplied by the corresponding age groups of a **standard population** to calculate standardized rates. *Standardized mortality ratio is used* - The **Standardized Mortality Ratio (SMR)** is a measure used in *indirect* age standardization, not direct age standardization. - SMR compares the number of observed deaths in a study population to the number expected if the study population had the same age-specific death rates as a **standard population**.

Q: In the estimation of statistical probability, Z score is applicable to:

Normal distribution. ***Normal distribution*** - The **Z-score** (or standard score) is a measure of how many **standard deviations** an element is from the mean. It is specifically used when working with **normally distributed data**. - It allows for the comparison of scores from different normal distributions by standardizing them to a common scale. *Poisson distribution* - This distribution deals with the **number of events** occurring in a fixed interval of time or space, given a known average rate, and is not typically used with Z-scores directly. - It is a **discrete probability distribution**, unlike the continuous nature required for direct Z-score application. *Skewed distribution* - A skewed distribution has an **asymmetrical shape**, where points cluster more on one side of the mean. - Z-scores can be calculated for skewed distributions, but their interpretation as probabilities (e.g., using a standard normal table) is **not valid** because the data do not follow a bell-shaped curve. *Binomial distribution* - This distribution describes the **number of successes** in a fixed number of independent Bernoulli trials. - It is a **discrete probability distribution** and generally, Z-scores are not directly applied to it, although for a large number of trials, it can be approximated by a normal distribution.

Q: The two important values necessary for describing the variation in a series of observations are:

Mean and standard deviation. ***Mean and standard deviation*** - The **mean** provides a measure of the **central tendency**, representing the average value in the dataset. - The **standard deviation** quantifies the **dispersion** or spread of the data points around the mean, indicating the variability. *Median and standard deviation* - The **median** is a measure of **central tendency**, specifically the middle value, but it doesn't directly pair with standard deviation for describing overall variation in the most common statistical contexts. - While standard deviation describes spread, using the median for central tendency often leads to other measures of spread like **interquartile range (IQR)** for a more consistent representation of non-normally distributed data. *Mean and range* - The **mean** indicates the central point of the data, but the **range** (difference between maximum and minimum values) is a less robust measure of variation. - **Range** is highly susceptible to outliers and does not provide information about the distribution of data points within the entire set. *Median and range* - The **median** describes the **center** of the data, particularly useful for skewed distributions or data with outliers. - The **range** is a simple measure of spread, but it's very sensitive to extreme values and does not give a comprehensive picture of data variability.

Q: Which one of the following tests should be applied to compare mean haemoglobin level of two groups of antenatal mothers?

Unpaired t-test. ***Unpaired t-Test*** - The **unpaired t-test** is used to compare the means of **two independent groups** on a continuous variable, such as hemoglobin levels. - Antenatal mothers in two distinct groups are independent, and **hemoglobin level is a continuous variable**, making this the appropriate choice. *Analysis of variance* - **ANOVA** (Analysis of Variance) is used to compare the means of **three or more independent groups**. - Since there are only **two groups** being compared, ANOVA is not the most efficient or appropriate test. *Chi-square test* - The **Chi-square test** is used to analyze the association between **two categorical variables**. - Hemoglobin level is a **continuous variable**, not categorical, so this test is not suitable for comparing means. *Paired t-test* - The **paired t-test** is used to compare the means of **two related groups** or the same group measured at two different times (e.g., before and after an intervention). - The two groups of antenatal mothers are **independent**, not paired or related.

Question 1

What is the Standardized Mortality Ratio (SMR) for the hazardous industry workers (as compared to national population)?

Accepted Answer

120

Answer

110

Answer

100

Answer

130

Question 2

Consider the following:

1. Standard deviation
2. Range
3. Mode
4. Median

Among the above, which is/are the measure/measures of dispersion?

Accepted Answer

1 and 2 only

Answer

3 and 4 only

Answer

1 only

Answer

1, 2, 3 and 4

Question 3

With reference to historical cohort study, which of the following statements is not correct?

Accepted Answer

Control subjects are selected from the current population without exposure.

Answer

Duration of study is shorter as compared to current cohort study.

Answer

Experience of cohort is assessed from existing records.

Answer

Outcomes have occurred before the start of the investigation.

Question 4

Specificity of a test is =

Accepted Answer

(True negatives X 100) / (True negatives + False positives) %

Answer

(True positives X 100) / (True negatives + False positives) %

Answer

(True positives X 100) / (True positives + False negatives) %

Answer

(True negatives X 100) / (True positives + True negatives) %

Question 5

In a normal curve, the area between one standard deviation on either side of the mean will include:

Accepted Answer

Approximately 68% of the values

Answer

70 – 85% of the values

Answer

95% of the values

Answer

Less than 50% of the values

Question 6

The relationship between birth rate and maternal hemoglobin is best studied by:

Accepted Answer

Correlation and regression.

Answer

Sensitivity and specificity.

Answer

Standard error of difference between two means.

Answer

Standard error of difference between two proportions.

Question 7

Which of the following statements is true about direct age standardization?

Accepted Answer

A standard population is used

Answer

Number of people in each age group is not known

Answer

Age specific death rates are not known

Answer

Standardized mortality ratio is used

Question 8

In the estimation of statistical probability, Z score is applicable to:

Accepted Answer

Normal distribution

Answer

Poisson distribution

Answer

Skewed distribution

Answer

Binomial distribution

Question 9

The two important values necessary for describing the variation in a series of observations are:

Accepted Answer

Mean and standard deviation

Answer

Median and standard deviation

Answer

Mean and range

Answer

Median and range

Question 10

Which one of the following tests should be applied to compare mean haemoglobin level of two groups of antenatal mothers?

Accepted Answer

Unpaired t-test

Answer

Analysis of variance

Answer

Chi-square test

Answer

Paired t-test

Biostatistics — MCQs

Biostatistics — MCQs

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