Biostatistics Practice Questions

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: 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: 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.

Q: Which of the following is true about Simple Random Sampling?

Every person has an equal chance of selection. ***Every person has an equal chance of selection*** - In **simple random sampling**, each member of the population has an **identical probability** of being chosen for the sample. - This method ensures **unbiased selection** from the population, as every element is given an equal opportunity. *Fewer samples are collected* - The number of samples collected is not inherently less; simple random sampling can involve any sample size, small or large. - This statement does not define a characteristic unique to or consistently true for simple random sampling. *Also known as Systematic randomization* - Simple random sampling is distinct from **systematic randomization**, which involves selecting every nth element from a list after a random start. - **Systematic randomization** follows a fixed interval, while **simple random sampling** involves individual random selections. *Groups may not be equally represented in small samples* - While possible, this is a limitation of all small samples and NOT particular to simple random sampling rather than a defining truth. - In small samples, **random chance** can lead to disproportional representation of subgroups, but this isn't a fundamental characteristic of the method itself.

Q: In a case-control study, which measure quantifies the strength of association between exposure and outcome?

Odds ratio (OR). ***Odds ratio (OR)*** - The **odds ratio** is the measure of association typically used in **case-control studies** because it quantifies the odds of exposure among cases compared to the odds of exposure among controls. - It is an effective estimate of the **relative risk** when the outcome is rare. *Relative risk (RR)* - **Relative risk** is used in **cohort studies** and **randomized controlled trials** as it measures the incidence of the outcome in the exposed group compared to the unexposed group. - Calculation of RR requires knowledge of the **incidence** or **prevalence of the outcome** in both exposed and unexposed groups, which is typically not directly available in case-control studies. *Attributable risk* - **Attributable risk** (also known as risk difference) measures the **absolute difference in risk** between exposed and unexposed groups. - It indicates the **proportion of disease** in the exposed group that can be attributed to the exposure, and is primarily used in **cohort studies** for public health impact assessment. *Hazard ratio (HR)* - The **hazard ratio** is used in **survival analysis** to compare the hazard rates (instantaneous incidence rates) between two groups over time. - It is typically seen in **clinical trials** or **cohort studies** to assess the effect of an intervention or exposure on the time to an event, such as death or disease recurrence.

Q: The denominator of the positive predictive value is

True positive + False positive. ***True positive + False positive*** - The **positive predictive value** (PPV) is defined as the probability that subjects with a **positive screening test** truly have the disease. - The formula for PPV is **True Positives / (True Positives + False Positives)**; thus, the denominator includes all positive test results. *False positive + True negative* - This combination of values describes the denominator for the **false positive rate** (False Positives / (False Positives + True Negatives)), which is not related to the PPV. - **True negatives** are correctly identified as not having the disease, which is irrelevant for the calculation of PPV. *False positive + false negative* - This sum does not directly represent any standard epidemiological measure or denominator for common test performance metrics like sensitivity, specificity, or PPV. - Both **False positives** and **False negatives** represent incorrect test outcomes. *True positive + False negative* - This represents the total number of individuals who **actually have the disease** (True Positives / (True Positives + False Negatives)) and is the denominator for **sensitivity**. - **False negatives** are individuals with the disease who tested negative, which are not relevant for the denominator of PPV.

Q: Which of the following best reflects the diagnostic power of a test?

Sensitivity and specificity. ***Sensitivity and specificity*** - **Diagnostic power of a test** refers to its intrinsic ability to correctly identify individuals with and without disease, which is best reflected by **sensitivity and specificity**. - **Sensitivity** (true positive rate) measures the test's power to detect disease when present - the ability to correctly identify diseased individuals. - **Specificity** (true negative rate) measures the test's power to rule out disease when absent - the ability to correctly identify non-diseased individuals. - These are **inherent properties of the test** that remain constant regardless of disease prevalence in the population, making them the true measures of diagnostic power. - Together, they define how well a test can discriminate between diseased and non-diseased states. *Predictive value of a test* - **Predictive values** (positive and negative) indicate the probability of disease given a test result, but they are measures of **clinical utility**, not diagnostic power. - Predictive values are **dependent on disease prevalence** - the same test with identical sensitivity and specificity will have different predictive values in populations with different disease prevalence. - They answer "Given this result, what is the probability of disease?" rather than measuring the test's inherent diagnostic ability. *Specificity alone* - **Specificity alone** is incomplete as it only measures the test's ability to identify non-diseased individuals. - Diagnostic power requires assessment of both the ability to detect disease (sensitivity) and to rule it out (specificity). *Population attributable risk of a test* - **Population attributable risk (PAR)** is an epidemiological measure that quantifies the proportion of disease in a population attributable to a specific risk factor. - It is not a measure of diagnostic test performance and is unrelated to diagnostic power.

Question 1

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 2

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 3

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 4

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 5

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

Question 6

Which of the following is true about Simple Random Sampling?

Accepted Answer

Every person has an equal chance of selection

Answer

Fewer samples are collected

Answer

Also known as Systematic randomization

Answer

Groups may not be equally represented in small samples

Question 7

In a case-control study, which measure quantifies the strength of association between exposure and outcome?

Accepted Answer

Odds ratio (OR)

Answer

Attributable risk

Answer

Hazard ratio (HR)

Answer

Relative risk (RR)

Question 8

What does the P-value represent in hypothesis testing?

Accepted Answer

The probability of obtaining results as extreme or more extreme than observed, assuming the null hypothesis is true.

Answer

The probability of not rejecting the null hypothesis when it is true.

Answer

The probability of rejecting the null hypothesis when it is false.

Answer

The probability of observing the data given that the null hypothesis is false.

Question 9

The denominator of the positive predictive value is

Accepted Answer

True positive + False positive

Answer

False positive + True negative

Answer

False positive + False negative

Answer

True positive + False negative

Question 10

Which of the following best reflects the diagnostic power of a test?

Accepted Answer

Sensitivity and specificity

Answer

Specificity alone

Answer

Population attributable risk of a test

Answer

Predictive value of a test

Biostatistics — MCQs

Biostatistics — MCQs

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