4. Biostatistics_1

What is biostatistics?

The application of statistical principles and methods to biological, medical, and public health data.

What does biostatistics involve?

The design, collection, analysis, interpretation, and presentation of data.

Why is biostatistics important in health sciences?

It supports evidence-based decision-making.

What scientific questions does biostatistics help answer?

It helps answer research questions, test hypotheses, and support health decisions.

What are three essential applications of biostatistics?

Disease surveillance, clinical research, and environmental exposure assessment.

What field forms the backbone of epidemiology and health policy?

Biostatistics.

Name four importance of biostatistics.

Disease surveillance, health planning, policy formulation, and evaluating interventions.

How does biostatistics help health systems?

It quantifies disease burden and guides resource allocation.

How does biostatistics improve patient care?

It enhances diagnostic accuracy and treatment outcomes.

What are the two main types of data?

Qualitative data and quantitative data.

What are the two subtypes of qualitative data?

Nominal and ordinal.

What are the two subtypes of quantitative data?

Discrete and continuous.

What is qualitative data?

Non-numerical data that describe qualities, characteristics, or categories.

How is qualitative data commonly summarized?

Using counts, percentages, or the mode.

What is nominal data?

Categories with no natural order or ranking.

Give three examples of nominal data.

Gender, blood group, and marital status.

Can mathematical averages be calculated for nominal data?

No.

What is ordinal data?

Categorical data with a meaningful order but unequal intervals between categories.

Give three examples of ordinal data.

Pain severity, education level, and Likert scale responses.

What is the key feature of ordinal data?

Categories can be ranked, but the differences between ranks are not equal.

What is quantitative data?

Numerical data representing counts or measurements.

What mathematical operations can be performed on quantitative data?

Addition, subtraction, averaging, and other calculations.

What is discrete data?

Countable whole-number data.

Give three examples of discrete data.

Number of children, hospital visits, and students in a class.

Can discrete data include fractions?

No.

What is continuous data?

Measured data that can take any value within a range.

Give five examples of continuous data.

Height, weight, blood pressure, temperature, and time.

What question does nominal data answer?

"What type is it?"

What question does ordinal data answer?

"What rank or level?"

What question does discrete data answer?

"How many?"

What question does continuous data answer?

"How much?"

What is a variable in research?

A characteristic, quantity, or attribute that can be measured, manipulated, or controlled.

Why is classifying variables important?

It is essential for study design, analysis, and interpretation.

What is an independent variable?

The presumed cause or influencing factor manipulated or selected by the researcher.

What is another name for an independent variable?

Predictor, explanatory, or exposure variable.

What is the purpose of an independent variable?

To test its influence on the dependent variable.

In a drug study, what is the independent variable?

Drug dosage.

What is a dependent variable?

The measured outcome or effect that depends on the independent variable.

What is another name for a dependent variable?

Outcome, response, or output variable.

In a drug study, what is the dependent variable?

Blood pressure.

What relationship is expected between the independent and dependent variables?

Changes in the independent variable cause changes in the dependent variable.

What is a confounding variable?

A third variable associated with both the independent and dependent variables that distorts their relationship.

Why are confounding variables a problem?

They can lead to incorrect conclusions.

Name three ways to control confounding variables.

Randomization, restriction, and matching.

Name two statistical methods used to adjust for confounding.

Stratification and multivariable regression.

What is the classic example of a confounder?

Smoking in the relationship between coffee drinking and lung cancer.

What is an effect modifier?

A variable that changes the strength or direction of the relationship between the independent and dependent variables.

Does an effect modifier distort the relationship?

No. It shows that the effect differs between groups.

How can an effect modifier be identified?

Through stratified analysis or interaction terms.

What is descriptive statistics?

Statistics used to summarize and organize data without making predictions.

What are the three main components of descriptive statistics?

Measures of central tendency, measures of dispersion, and graphical presentation.

What are the three measures of central tendency?

Mean, median, and mode.

What is the mean?

The arithmetic average.

Which measure of central tendency is most affected by outliers?

The mean.

What is the median?

The middle value in an ordered dataset.

When is the median most useful?

For skewed data.

What is the mode?

The most frequently occurring value.

When is the mode especially useful?

For categorical data.

What are measures of dispersion?

Statistics that describe how spread out data are.

Why are measures of dispersion important?

They describe variability, consistency, and reliability of data.

What are the four common measures of dispersion?

Range, interquartile range (IQR), variance, and standard deviation.

What is the range?

The difference between the maximum and minimum values.

Formula for range?

Maximum value − Minimum value.

What is the interquartile range (IQR)?

The spread of the middle 50% of the data.

Formula for IQR?

Q3 − Q1.

When is the IQR especially useful?

For skewed data and identifying outliers.

What is variance?

The average squared deviation from the mean.

What is standard deviation?

The square root of the variance expressed in the original units.

Which measure of dispersion is most commonly reported?

Standard deviation.

Which measure of dispersion is most sensitive to outliers?

Range.

Which measure has low sensitivity to outliers?

Interquartile range (IQR).

Which measure is commonly used for statistical modelling?

Variance.

Which measure is commonly reported for normally distributed data?

Standard deviation.

Name five methods of presenting data.

Tables, bar charts, histograms, pie charts, and box-and-whisker plots.

What is a table?

A systematic arrangement of data in rows and columns.

What is a bar chart used for?

Comparing discrete categories.

What is a histogram used for?

Displaying the distribution of continuous numerical data.

What is a pie chart used for?

Showing proportions or percentages of a whole.

What information does a box-and-whisker plot display?

The minimum, Q1, median, Q3, maximum, and potential outliers.