Probability & Inference in Biostatistics (MAST90100)

Intended learning outcomes

On completion of this subject, students should be able to:

• Demonstrate an understanding of the fundamental concepts of probability, including discrete and continuous probability distributions
• Apply calculus-based techniques to derive key features of a probability distribution and properties of random variables, such as mean and variance
• Recognise common probability distributions and their properties
• Demonstrate the role of simulation of random variables in understanding and explaining random variation and key ideas of statistical inference
• Explain and employ the key elements of statistical inference: target parameters, sampling variability, and frequentist methods including confidence intervals and hypothesis testing
• Explain the role of the likelihood function in parametric statistical inference, and be able to derive and interpret likelihood-based estimates for standard models
• Explain Bayesian concepts of post-data uncertainty and derive Bayesian inferences for simple standard models
• Critically interpret the commonly used tools of statistical inference, such as p-values, confidence intervals and Bayesian posterior distributions, as they are used in medical and scientific investigations

Generic skills

• Independent problem solving,
• Facility with abstract reasoning,
• Clarity of written expression,
• Sound communication of technical concepts