Core Course 3: Data Analysis for Public Health
- Descriptive statistics, data tables and graphs
- Elementary probability theory, probability distributions, diagnostic tests and their properties
- Hypothesis testing and confidence intervals — calculating, constructing and interpreting
- Understanding limitations and best practices for using p-values and confidence intervals
- Selecting and evaluating methods of analysis that are appropriate for answering research questions for a given study design
- Evaluation of straightforward biostatistical usage in public health, with an emphasis on understanding research and scholarly publications
- Evaluation of the assumptions for statistical tests
- Common distributions and why they are important
- Analysis of different types of data such as categorical outcome, continuous outcome and binary outcome
- Explain the role of quantitative methods and sciences in describing and assessing a population’s health.
- Analyze quantitative data using biostatistics, informatics, computer-based programming and software, as appropriate.