Health Statistics Course

Lesson 1 • Ethical Principles in Health Data

Introduces confidentiality, informed consent, and data governance obligations in health research. Ethical compliance is prerequisite to responsible statistical practice.

Lesson 2 • Core Concepts and Terminology

Defines health statistics, its scope, and key vocabulary used throughout the field. Establishes shared language needed for all subsequent analytical work.

Lesson 3 • Types of Health Data

Distinguishes categorical, ordinal, and continuous data types common in health contexts. Correct data classification drives appropriate analytical method selection.

Lesson 4 • Measurement Scales and Levels

Covers nominal, ordinal, interval, and ratio scales with health examples. Scale level determines permissible statistical operations on collected data.

Lesson 1 • Data Visualization in Health

Selects and constructs histograms, box plots, bar charts, and scatter plots for health data. Effective visualization accelerates pattern recognition and stakeholder communication.

Lesson 2 • Frequency Distributions and Tables

Constructs frequency, relative frequency, and cumulative frequency tables for health variables. Tabular summaries are the first step in exploring any health dataset.

Lesson 3 • Describing Distributions and Shape

Assesses skewness, kurtosis, and normality of health data distributions. Distribution shape determines which inferential methods are valid in later analyses.

Lesson 4 • Measures of Central Tendency

Calculates mean, median, and mode for health data and selects the appropriate measure by distribution shape. Central tendency metrics anchor all comparative health analyses.

Lesson 1 • Mortality and Survival Rates

Computes crude, cause-specific, and case-fatality rates alongside basic survival metrics. Mortality rates are primary indicators of population health status.

Lesson 2 • Ratios, Proportions, and Percentages

Distinguishes ratios, proportions, and percentages and applies each to health data correctly. These foundational metrics underpin all disease frequency measures.

Lesson 3 • Fertility and Natality Measures

Calculates crude birth rate, general fertility rate, and total fertility rate from vital statistics. Natality measures complement mortality data in describing population dynamics.

Lesson 4 • Incidence and Prevalence Measures

Defines and calculates incidence rate, cumulative incidence, and prevalence for health conditions. These measures quantify disease burden and inform resource allocation decisions.

Lesson 1 • The Normal Distribution

Describes properties of the normal distribution and uses z-scores to calculate health-related probabilities. Normality assumptions underpin many parametric statistical tests.

Lesson 2 • Fundamentals of Probability

Covers classical, empirical, and subjective probability with health applications. Probability concepts underlie all inferential statistics and risk communication.

Lesson 3 • Bayes' Theorem in Diagnostics

Applies Bayes' theorem to update disease probability given test results. This section links probability theory directly to clinical diagnostic reasoning.

Lesson 4 • Other Key Distributions

Introduces t, chi-square, and F distributions as foundations for inferential testing. Recognizing these distributions prepares students for hypothesis testing in the next chapter.

Lesson 1 • Experimental and Quasi-Experimental Designs

Describes randomized controlled trials and quasi-experimental alternatives for health interventions. Experimental designs provide the strongest evidence for causal health claims.

Lesson 2 • Sample Size Determination

Calculates required sample sizes for means, proportions, and comparative studies. Adequate sample size ensures sufficient statistical power to detect meaningful health differences.

Lesson 3 • Principles of Sampling

Explains representativeness, sampling frames, and sources of sampling error. Sound sampling is the prerequisite for generalizable health research findings.

Lesson 4 • Probability Sampling Methods

Covers simple random, systematic, stratified, and cluster sampling with health examples. Each method offers different trade-offs between precision and logistical feasibility.

Lesson 1 • Analysis of Variance

Applies one-way and two-way ANOVA to compare health outcomes across multiple groups. ANOVA extends mean comparison to multi-group health intervention studies.

Lesson 2 • Tests for Proportions and Counts

Uses chi-square tests, Fisher's exact test, and z-tests for proportions in health data. These tests evaluate associations between categorical health variables.

Lesson 3 • Estimation and Confidence Intervals

Constructs point estimates and confidence intervals for means, proportions, and rates. Interval estimation quantifies uncertainty around health parameter estimates.

Lesson 4 • Tests for Means

Applies one-sample, independent-samples, and paired t-tests to health outcome data. Mean comparison tests address the most common quantitative health research questions.

Lesson 1 • Logistic Regression for Binary Outcomes

Models binary health outcomes such as disease presence using logistic regression. Logistic regression produces odds ratios essential for epidemiological risk analysis.

Lesson 2 • Multiple Linear Regression

Extends regression to multiple predictors for health outcomes while controlling for confounders. Multiple regression is the standard tool for adjusted health outcome modeling.

Lesson 3 • Correlation Analysis

Measures linear and rank-based associations between health variables using Pearson and Spearman coefficients. Correlation quantifies relationship strength without implying causation.

Lesson 4 • Simple Linear Regression

Fits and interprets a simple linear regression model for a continuous health outcome. Regression extends correlation to prediction and quantifies the effect of one predictor.

Lesson 1 • Survival Analysis Fundamentals

Applies Kaplan-Meier estimation and log-rank tests to time-to-event health data. Survival analysis handles censored observations common in clinical and cohort studies.

Lesson 2 • Confounding and Effect Modification

Identifies confounding variables and effect modifiers using stratified analysis and statistical adjustment. Controlling confounding is essential for valid causal inference in health studies.

Lesson 3 • Measures of Association and Effect

Calculates relative risk, odds ratio, attributable risk, and population attributable fraction. These measures quantify the strength and public health impact of risk factor associations.

Lesson 4 • Systematic Review and Meta-Analysis

Synthesizes evidence from multiple health studies using pooled effect estimates and heterogeneity assessment. Meta-analysis produces the highest-level quantitative evidence summaries.