Applied Statistics for the Health Sciences: Introduction and Foundations

Applied Statistics for the Health Sciences

Course Introduction and Relevance

This course provides foundational knowledge in statistics, specifically tailored for health sciences students. Understanding statistical principles is essential for interpreting research, evaluating evidence, and making informed decisions in clinical and public health practice.

• Importance: Statistics helps determine disease prevalence, risk factors, and the effectiveness of interventions.

• Application: Skills learned are directly applicable to research projects and evidence-based practice.

• Benefit: A better understanding of statistics enhances professional practice and research capabilities.

Course Structure and Resources

• Moodle Platform: All course materials, syllabus, lecture slides, PDFs, and group project instructions are available on Moodle.

• Syllabus: The syllabus outlines topics, grading, and expectations for the course.

• Success Tips:

  • Read assigned chapters before each class.

  • Take notes and participate in discussions.

  • Complete textbook exercises.

  • Review material before exams.

The Research Process

Overview of the Research Process

Research in health sciences follows a systematic process to answer specific questions and test hypotheses. The process typically includes:

• Formulating a research question

• Designing the study

• Collecting data

• Analyzing data

• Interpreting results

The Three Research Components

Every research study involves three key components:

• Research Design: The overall strategy for integrating different parts of the study to address the research question.

• Variable Measurement: Identifying and quantifying the variables of interest.

• Statistical Analysis: Applying statistical methods to summarize and interpret data.

Research Design

• Defines objectives and hypotheses

• Determines study type (e.g., experimental, observational)

• Guides selection of research methods

Variable Measurement

• Identifies what needs to be measured (e.g., blood pressure, age, recovery time)

• Specifies units and methods of measurement

• Examples: Number of patients, hours after an operation, frequency of symptoms

Statistical Analysis

• Involves collecting, organizing, summarizing, and interpreting data

• Uses descriptive statistics (mean, standard deviation, frequency) and inferential statistics (hypothesis testing, confidence intervals)

• Provides evidence to support or refute hypotheses

Types of Statistical Analyses

Descriptive vs. Inferential Statistics

Statistical analyses are broadly classified into two types:

• Descriptive Statistics: Summarize and describe features of a dataset.

  • Examples: Mean, median, mode, standard deviation, frequency distributions

• Inferential Statistics: Make predictions or inferences about a population based on sample data.

  • Examples: Hypothesis testing, confidence intervals, regression analysis

Comparison of Experimental and Observational Studies

Study Type	Definition	Examples
Experimental	Researcher manipulates variables to observe effects	Clinical trials, laboratory experiments
Observational	Researcher observes without intervention	Cohort studies, case-control studies, surveys

Examples of Research Scenarios

Example 1

A team of researchers wants to measure the effects of stress reduction interventions on blood pressure in patients before and after surgery. They record blood pressure readings and compare results.

• Objectives: Measure intervention effects

• Research Type: Experimental

• Analysis: Compare means before and after intervention

Example 2

A researcher wants to know the proportion of the population suffering from chronic anxiety. They collect data from a random sample and calculate the percentage.

• Objectives: Estimate prevalence

• Research Type: Observational

• Analysis: Calculate proportions and confidence intervals

Example 3

A team wants to identify lifestyle habits associated with certain symptoms. They collect information on diet, exercise, and other habits.

• Objectives: Identify associations

• Research Type: Observational

• Analysis: Correlation and regression analysis

Variable Measurement

Types of Variables

Variables are classified based on how they are measured:

• Qualitative (Categorical) Variables: Not measured numerically; represent categories or groups.

  • Examples: Gender, ethnicity, region

• Quantitative Variables: Measured numerically; represent amounts or counts.

  • Examples: Age, temperature, blood pressure

Levels of Measurement

Level	Description	Examples
Nominal	Categories without order	Gender, ethnicity
Ordinal	Ordered categories	Severity of symptoms (mild, moderate, severe)
Interval	Numerical, equal intervals, no true zero	Temperature (Celsius)
Ratio	Numerical, equal intervals, true zero	Height, weight, age

Measurement Examples

• Types of anesthesia (nominal)

• Region of residence (nominal)

• Age of patients (ratio)

• Temperature before/after surgery (interval/ratio)

• Ethnicity of patients (nominal)

Conclusion

Defining research questions and variables is essential for designing studies and selecting appropriate statistical analyses. The research design and measurement of variables determine which statistical tests are suitable for analyzing data and drawing valid conclusions.

Next Steps

The next class will focus on statistical analysis, building on the concepts of research design and variable measurement.