Biostatistics Practice Questions

Q: Which of the following is not considered a type of subject bias?

Selection bias. ***Selection bias*** - **Selection bias** occurs when participants are chosen or remain in a study in a way that introduces a systematic error, leading to a sample that does not accurately represent the target population. - It is a **study design and sampling issue** that occurs at the **recruitment** or **retention stage**, not a bias arising from the subjects' own behavior or reporting. - Unlike subject biases, selection bias is introduced by the **investigators or study methodology**, not by the participants themselves. *Recall bias* - **Recall bias** is a type of **subject bias** where participants differentially remember and report past exposures based on their outcome status. - Subjects with disease may recall exposures more accurately than healthy controls, introducing **systematic error from the subject's memory**. *Hawthorne bias* - **Hawthorne bias** (observer effect) is a **subject bias** where participants modify their behavior because they know they are being studied. - The **subject's awareness** of observation directly influences their actions, responses, or adherence. *Reporting bias* - **Reporting bias** is a **subject bias** where participants selectively disclose or withhold information based on social desirability, embarrassment, or perceived consequences. - This bias arises from the **subject's decision** about what to report.

Q: What is the correct formula for calculating the positive predictive value (PPV) of a screening test?

True positives / (True positives + False positives). ***True positives / (True positives + False positives)*** - **Positive predictive value (PPV)** indicates the probability that a patient who tests positive actually has the disease. - It is calculated by dividing the number of **true positives** (correctly identified positive cases) by the total number of positive test results (**true positives + false positives**). *True positives / (True positives + False negatives)* - This formula represents the **sensitivity** of a test, which is the proportion of actual positive cases that are correctly identified. - Sensitivity measures the ability of a test to correctly identify individuals with the disease. *False positives / (False positives + True negatives)* - This formula represents **1 - specificity**, or the **false positive rate**. - **Specificity** is the proportion of actual negative cases that are correctly identified as negative. *True negatives / (True negatives + False negatives)* - This formula represents the **negative predictive value (NPV)**, which is the probability that a patient who tests negative actually does not have the disease. - NPV is calculated by dividing the number of **true negatives** (correctly identified negative cases) by the total number of negative test results (**true negatives + false negatives**).

Q: Central value of a set of 180 values can be obtained by which of the following?

2nd quartile. ***2nd quartile*** - The **2nd quartile** is equivalent to the **median**, which represents the central value of a dataset. - For 180 values, the median (Q2) would be located between the 90th and 91st values when ordered, effectively dividing the data into two equal halves. *2nd tertile* - The 2nd tertile divides the data into three equal parts, signifying the value below which two-thirds of the data lie, not the central value. - For 180 values, the 2nd tertile would be around the 120th ordered value (2/3 of 180), which is far from the center. *80th percentile* - The 80th percentile indicates that 80% of the data falls below this value. - This is a measure of a specific position within the upper portion of the data, not the central tendency. *9th decile* - The 9th decile represents the value below which 90% of the data falls. - This value is very high in the dataset and does not represent the central value.

Q: The fundamental equilibrium principle of population genetics was given by?

Hardy Weinberg. ***Hardy Weinberg*** - The **Hardy-Weinberg principle** describes the conditions under which allele and genotype frequencies in a population remain constant from generation to generation. - It established the baseline for understanding when evolutionary forces like **mutation**, **selection**, **gene flow**, and **genetic drift** are acting on a population. *Sewall Wright* - Sewall Wright is known for his work on **genetic drift**, particularly the concept of the **effective population size** and the **shifting balance theory** of evolution. - While fundamental to population genetics, his contributions did not lay the initial equilibrium principle. *J. B. S. Haldane* - J.B.S. Haldane made significant contributions to the **mathematical theory of natural selection** and was a pioneer in developing population genetics as a field. - He focused more on the dynamics of evolution under selection rather than the foundational equilibrium state. *R. A. Fisher* - R. A. Fisher was a key figure in modern statistics and population genetics, known for developing concepts like **Fisher's fundamental theorem of natural selection** and the **evolution of dominance**. - His work built upon the Hardy-Weinberg equilibrium, explaining how selection drives evolutionary change.

Q: Which scale is also known as the Cumulative Scale?

Guttman Scale. ***Guttman Scale*** - The **Guttman scale**, also known as the **cumulative scale**, is designed such that if an individual agrees with a more extreme statement, they will also agree with all less extreme statements - It measures a **single, unidimensional trait**, and responses are ordered cumulatively - This cumulative property is what gives it the name "Cumulative Scale" *Visual Analog Scale* - The **Visual Analog Scale (VAS)** is a psychometric response scale used to measure subjective characteristics or attitudes that cannot be directly measured - It typically presents a **continuous line** where patients mark their current state, most commonly used for pain assessment - Not a cumulative scale *Thurstone Scale* - A **Thurstone scale** uses a panel of judges to assign numeric values to attitude statements based on their perceived intensity or favorability - It aims to create an **interval scale** where the distance between categories is assumed to be equal - Does not have cumulative properties *Semantic Differential Scale* - The **Semantic Differential Scale** measures the connotative meaning of concepts or objects - Asks respondents to rate a concept on a series of **bipolar adjective pairs** (e.g., good-bad, strong-weak) - Used to assess perceptions and attitudes rather than cumulative agreement

Q: What is the effect of increasing the confidence level in hypothesis testing?

Increased significance threshold affects results. ***Increased significance threshold affects results*** - Increasing the **confidence level** (e.g., from 95% to 99%) means we are demanding higher certainty that our result is not due to random chance. This translates to a **lower alpha (significance level)** - from α=0.05 to α=0.01. - A higher confidence level implies a **more stringent threshold** for rejecting the null hypothesis. The p-value must now be smaller than the reduced alpha to achieve statistical significance. - This makes it **harder to reject the null hypothesis** and reduces the probability of Type I error (false positive). *Previously significant value remains significant* - This statement is incorrect because if a **p-value** was barely significant at a lower confidence level (e.g., p=0.04 at 95% confidence, α=0.05), it would become **non-significant** at a higher confidence level (e.g., 99% confidence, α=0.01). - The threshold for **statistical significance** becomes stricter, meaning fewer results will meet the criteria. *Hypothesis testing outcome may change* - While this is technically true, it is less precise than the correct answer. The outcome may change specifically because results that were previously significant may become non-significant. - This option describes a **consequence** rather than the direct effect of changing the confidence level. *Previously insignificant value may become significant* - This statement is incorrect. If a result was **non-significant** at a lower confidence level (e.g., p=0.06 at 95% confidence, α=0.05), it will certainly remain non-significant at a higher confidence level (e.g., 99% confidence, α=0.01). - Increasing the confidence level makes it **harder, not easier** to achieve statistical significance by requiring a smaller p-value to reject the null hypothesis.

Q: Which type of epidemiological study uses a population as the unit of study?

Ecological study. ***Correct - Ecological study*** - An **ecological study** examines exposure-outcome relationships by using **data aggregated at the population level**, rather than individual data. - The unit of analysis is a group (e.g., countries, regions, schools), making it ideal for studying population-level trends and associations. *Incorrect - Cohort study* - A **cohort study** follows a group of individuals (the cohort) over time to determine the incidence of a disease or outcome based on their **exposure status**. - The unit of study is the **individual**, observed prospectively or retrospectively. *Incorrect - Case-control study* - A **case-control study** compares individuals with a disease (cases) to individuals without the disease (controls) to identify past **exposures associated with the disease**. - The unit of study is the **individual**, and it is retrospective in nature. *Incorrect - Experimental study* - An **experimental study** (e.g., a randomized controlled trial) involves an **intervention** applied to a group of individuals and compares outcomes with a control group. - The unit of study is typically the **individual** or a small group, with researchers controlling exposure.

Q: A scatter diagram is drawn to study the relationship between two quantitative variables. What does it primarily illustrate?

Relationship between two given variables.. ***Relationship between two given variables*** - A **scatter diagram**, also known as a scatter plot, is specifically designed to visualize the **relationship** or **correlation** between two different quantitative variables. - Each point on the plot represents a pair of values (x, y) for the two variables, allowing for the observation of patterns, clusters, or trends. *Frequency of occurrence of events in categorical data.* - **Bar charts** or **pie charts** are typically used to illustrate the frequency of occurrence of events in categorical or qualitative data. - Scatter diagrams are not suited for displaying **categorical data frequencies**. *Mean and median values of the given data.* - **Box plots** or **histograms** are better suited for illustrating the mean, median, and distribution of a single variable. - A scatter diagram shows individual data points and their relationship, not summary statistics like **mean** or **median**. *Trend of a variable over time in a time series analysis.* - A **line graph** or **time series plot** is used to show the trend of a variable over time. - While a scatter plot can show **patterns**, it does not inherently represent the sequential nature of time series data unless time is one of the plotted variables.

Q: What correlation coefficient indicates the strongest positive correlation between two variables?

1. ***1*** - A correlation coefficient of **1** signifies a **perfect positive linear relationship** between two variables, meaning as one variable increases, the other increases proportionally. - This value represents the strongest possible positive correlation. *0* - A correlation coefficient of **0** indicates **no linear relationship** between the two variables. - Changes in one variable are not associated with predictable changes in the other. *0.7 to 0.9* - A correlation coefficient in this range indicates a **strong positive correlation**, but it is not the *strongest* possible. - While significant, it suggests that the relationship is not perfectly linear. *Greater than 1* - A correlation coefficient **cannot be greater than 1** or less than -1. - The range for the Pearson correlation coefficient is **-1 to +1**, inclusive.

Question 1

Which of the following is not considered a type of subject bias?

Accepted Answer

Selection bias

Answer

Recall bias

Answer

Hawthorne bias

Answer

Reporting bias

Question 2

What is the correct formula for calculating the positive predictive value (PPV) of a screening test?

Accepted Answer

True positives / (True positives + False positives)

Answer

True positives / (True positives + False negatives)

Answer

False positives / (False positives + True negatives)

Answer

True negatives / (True negatives + False negatives)

Question 3

Central value of a set of 180 values can be obtained by which of the following?

Accepted Answer

2nd quartile

Answer

2nd tertile

Answer

80th percentile

Answer

9th decile

Question 4

The fundamental equilibrium principle of population genetics was given by?

Accepted Answer

Hardy Weinberg

Answer

Sewall Wright

Answer

J. B. S. Haldane

Answer

R. A. Fisher

Question 5

Which scale is also known as the Cumulative Scale?

Accepted Answer

Guttman Scale

Answer

Thurstone Scale

Answer

Visual Analog Scale

Answer

Semantic Differential Scale

Question 6

What is the effect of increasing the confidence level in hypothesis testing?

Accepted Answer

Increased significance threshold affects results

Answer

Previously significant value remains significant

Answer

Hypothesis testing outcome may change

Answer

Previously insignificant value may become significant

Question 7

Which type of epidemiological study uses a population as the unit of study?

Accepted Answer

Ecological study

Answer

Cohort study

Answer

Experimental study

Answer

Case-control study

Question 8

In the context of hypothesis testing, what does statistical power refer to?

Accepted Answer

The probability of correctly rejecting a false null hypothesis.

Answer

The probability of failing to reject a true null hypothesis.

Answer

The probability of incorrectly rejecting a true null hypothesis.

Answer

The probability of incorrectly rejecting a false null hypothesis.

Question 9

A scatter diagram is drawn to study the relationship between two quantitative variables. What does it primarily illustrate?

Accepted Answer

Relationship between two given variables.

Answer

Frequency of occurrence of events in categorical data.

Answer

Mean and median values of the given data.

Answer

Trend of a variable over time in a time series analysis.

Question 10

What correlation coefficient indicates the strongest positive correlation between two variables?

Accepted Answer

1

Answer

0.7 to 0.9

Answer

0

Answer

Greater than 1

Biostatistics — MCQs

Biostatistics — MCQs

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