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Capella University — MPH Program

MPH5509: Principles of Biostatistics

A complete guide to Capella's MPH5509. Students learn the objectives, principles, and methods of biostatistics for health sciences, covering statistical terminology, descriptive analysis, probability, hypothesis testing, and analytical approaches for multiple data types.

Graduate4 CreditsMPH Program

MPH5509 delivers the biostatistical toolkit that public health professionals rely on — from statistical terminology and descriptive analysis through probability, sampling distributions, hypothesis testing, and the analytical approaches needed for continuous, binary, and survival data in health sciences research.

Biostatistics for public health practice

Core topics

  • Statistical terminology and descriptive analysis: Building fluency in statistical language and techniques for describing health data
  • Probability fundamentals: Understanding probability concepts and their applications in health sciences
  • Sampling distributions and hypothesis testing: Applying sampling theory and hypothesis testing methods to public health questions
  • Data type-specific analysis: Using analytical approaches appropriate for continuous, binary, and survival data

MPH5509 assignments include biostatistical analyses and hypothesis testing exercises

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Frequently asked questions

Why does MPH5509 cover survival data analysis alongside more basic statistical concepts?

Survival analysis (time-to-event analysis) is a core statistical method in public health — it's how researchers analyze outcomes like time to disease onset, treatment failure, or death, which are central to epidemiology and health outcomes research. Including it alongside descriptive statistics, probability, and hypothesis testing gives MPH students a complete biostatistical toolkit in a single course, covering both the foundational methods they'll use daily and the specialized technique they'll encounter most frequently in public health literature and practice. Without survival analysis, an MPH graduate would be unable to interpret much of the epidemiological research that informs public health policy.